AIR QUALITY MONITORING SYSTEM FOR IDENTIFYING CRITICAL POLLUTION ZONES USING REAL-TIME DATA FROM MOBILE SENSORS

Abstract

The present invention relates to a mobile air quality monitoring system (100) designed for real-time data collection and analysis using a mobile sensor network. The system (100) comprises a mobile air quality sensing unit (102) equipped with a plurality of air quality sensors (104A-N) deployed on vehicles to detect particulate matter (PM2.5 and PM10), environmental parameters such as temperature, humidity, and vehicle speed, and a plurality of GPS modules (106A-N) to collect geographic coordinates and timestamps. The collected data is transmitted to a server (110) for real-time processing. The server (110) employs Inverse Distance Weighting (IDW) spatial interpolation to estimate pollution levels at unsampled locations and uses machine learning (114) to analyze air quality patterns, forecast trends, and identify factors affecting pollution. A hotspot detection model (116) identifies clusters of high particulate concentrations, enabling the detection of critical pollution zones. This system (100) provides insights for improved environmental management. FIG. 1

Specification

Description:The present invention generally relates to the field of environmental monitoring systems, and more specifically to a mobile air quality monitoring system designed to estimate, analyze, and identify critical pollution zones in real-time.
Description of the Related Art
[0002] Current air quality monitoring predominantly relies on fixed Air Quality Index (AQI) stations, which provide accurate but highly localized data, capturing pollution levels within a narrow radius around the station. This limited reach is inadequate for representing air quality across larger geographical areas, where pollution can vary significantly due to factors like traffic, industrial activities, and natural phenomena. As a result, decision-makers lack a comprehensive view of air quality conditions, which hampers the design of effective interventions. Another significant limitation is the high cost associated with the deployment and maintenance of AQI stations. These stations require specialized equipment, infrastructure, and skilled personnel for calibration, making widespread deployment impractical. Consequently, AQI stations are typically concentrated in urban centers or known pollution zones, leaving vast regions, particularly suburban, rural, or remote areas, without adequate monitoring. This uneven distribution leads to data gaps and hinders comprehensive air quality management.
[0003] In addition, AQI stations require substantial physical space and infrastructure, posing challenges in areas with limited spatial resources, such as densely populated urban centers. This further restricts the placement of stations and contributes to uneven coverage of air quality data. In many regions, especially those less accessible or congested, pollution hotspots may remain undetected due to the absence of monitoring stations. The limited number of AQI stations directly affects the availability of comprehensive air quality data. Insufficient monitoring makes it difficult for environmental agencies, policymakers, and researchers to assess pollution trends, identify sources, or evaluate environmental policies effectively. This lack of data also undermines public trust in air quality reports, as incomplete or inaccurate information can lead to skepticism about reported pollution levels and health advisories. While fixed AQI stations offer accurate localized data, their limited coverage presents a significant challenge. Expanding fixed station networks to achieve full coverage is both cost-prohibitive and spatially impractical. Therefore, a solution is needed that provides widespread, accessible air quality monitoring without compromising data accuracy
[0004] Accordingly, there remains a need to address the aforementioned technical drawbacks in existing technologies in developing a mobile air quality monitoring system that provides an effective and scalable solution for estimating, analyzing, and identifying critical pollution zones.
SUMMARY
[0005] In view of the foregoing, the first aspect of the present invention provides a mobile air quality monitoring system for estimating, analyzing, and identifying critical pollution zones based on real-time data collection from a mobile sensor network. The system includes a mobile air quality sensing unit that includes a plurality of air quality sensors deployed on vehicles.Each sensor is configured to detect particulate matter (PM2.5 and PM10) of air and environmental parameters comprising temperature, humidity, and vehicle speed. A plurality of GPS modules are integrated into each air quality sensor that configured to collect geographic coordinates comprising latitude and longitude of the plurality of air quality sensors at a given moment, and timestamp of when the particulate matter and the environmental parameters were collected at a given location. The system includes a data processing unit communicably connected to the plurality of air quality sensors. The data processing unit is configured to (i) continuously collect air quality data comprising the particulate matter (PM2.5 and PM10) and the environmental parameters and real-time GPS data comprising the GPS coordinates from the plurality of air quality sensors (104A-N), and (ii) transmit the collected data to a server (110) via a GPRS module for real-time analysis. The system includes a server configured to estimate air quality levels at unsampled locations using the collected air quality and GPS data by applying Inverse Distance Weighting (IDW) spatial interpolation to generate an interpolated data. The pollution levels are estimated based closer data points having a higher influence. The server is configured to process the interpolated data using a machine learning model to (i) analyze air quality patterns, (ii) forecast future pollution trends, and (iii) identify factors affecting air quality comprising vehicle speed, traffic density, and environmental conditions. The server is configured toidentify statistically significant clusters of high particulate matter concentrations across various regions by implementing a hotspot detection model, on the machine learning outputs comprising the analyzed air quality patterns, the forecasted future pollution trends, and the identified factors affecting air quality. The server is configured todetect the critical pollution zones based on the identified clusters of the high particulate matter concentrations and the analyzed air quality patterns, providing actionable insights for targeted interventions and environmental management.
[0006] In an embodiment, the plurality of air quality sensors are equipped with a calibration module that periodically adjusts the sensor readings to account for drift and ensure measurement accuracy over time.
[0007] In another embodiment, the IDW spatial interpolation comprises a dynamic weighting mechanism that adjusts the influence of data points based on real-time sensor reliability and data freshness, thereby improving the estimation accuracy for air quality levels in unsampled areas.
[0008] In yet another embodiment, the machine learning model is configured to use time-series analysis to incorporate historical air quality data and predict the future pollution trends with temporal dependencies.
[0009] In yet another embodiment, the hotspot detection model includes a multi-scale analysis feature that identifies pollution hotspots across various spatial resolutions comprising neighborhood, city, and regional scales.
[0010] In yet another embodiment, the hotspot detection model comprises at least one of Getis-Ord Gi statistic or kernel density estimation to detect the critical pollution zones.
[0011] In yet another embodiment, the GPRS module used for data transmission is integrated with a network status monitoring function that assesses connectivity and signal strength, ensuring optimal data transmission performance and automatic re-transmission in case of failures.
[0012] In yet another embodiment, the machine learning model identifies unusual patterns or sudden changes in the air quality data, enabling proactive alerts and detailed analysis of potential pollution sources.
[0013] In yet another embodiment, the server is configured to combine the real-time GPS with the air quality data to generate pollution maps, wherein pollutant levels are overlaid on the geographical coordinates to visualize the spatial distribution of air pollution across different regions.
[0014] The second aspect of the present invention provides a method for estimating, analyzing, and identifying critical pollution zones based on real-time data collection using a mobile air quality monitoring system. The method includes (i) detecting particulate matter (PM2.5 and PM10) of air and environmental parameters comprising temperature, humidity, and vehicle speed using a mobile air quality sensing unit comprising a plurality of air quality sensors, the mobile air quality sensing unit is deployed on vehicles; (ii) collecting geographic coordinates comprising latitude and longitude of the air quality sensor at a given moment, and timestamp of when the particulate matter and the environmental parameters were collected at a given location using a GPS module integrated into each air quality sensor; (iii) continuously collecting air quality data comprising the particulate matter (PM2.5 and PM10) and the environmental parameters and real-time GPS data comprising the GPS coordinates from the plurality of air quality sensors by a data processing unit communicably connected to the plurality of air quality sensors; (iv) transmitting the collected air quality data and the real-time GPS data to a server via a GPRS module of the mobile air quality sensing unit for real-time analysis; (v) estimating air quality levels at unsampled locations using the collected air quality data and the real-time GPS data by applying Inverse Distance Weighting (IDW) spatial interpolation to generate an interpolated data, wherein pollution levels are estimated based on closer data points having a higher influence; (vii) processing the interpolated data using a machine learning model to analyze air quality patterns, forecast future pollution trends, and identify factors affecting air quality comprising vehicle speed, traffic density, and environmental conditions; (viii) identifying statistically significant clusters of high particulate matter concentrations across various regions by implementing a hotspot detection model, on the machine learning outputs comprising the analyzed air quality patterns, the forecasted future pollution trends, and the identified factors affecting air quality; and (ix) detecting the critical pollution zones based on the identified clusters of the high particulate matter concentrations and the analyzed air quality patterns, providing actionable insights for targeted interventions and environmental management.
[0015] The system offers substantial benefits for real-time air quality monitoring by equipping vehicles with air quality sensors and GPS modules. This setup enables dynamic detection of pollution levels as vehicles move through various areas, providing extensive coverage that surpasses the limitations of fixed AQI stations. By capturing pollution variations influenced by factors such as traffic and industrial activities, the system offers a more comprehensive view of air quality. Integrating GPS data with air quality measurements generates detailed pollution maps, pinpointing hotspots and tracking pollution sources. This supports informed decision-making for targeted interventions. Additionally, GPRS modules ensure seamless data transmission, with local SD card backup to prevent data loss during network failures. The system's scalability allows easy expansion by adding more sensor nodes as needed.
[0016] The GPS-enabled mobile air quality monitoring system provides wide coverage, collecting air quality data from various locations and offering a more accurate representation of air conditions across different regions. The real-time monitoring feature captures pollution variations due to traffic, construction, and other activities, ensuring immediate detection and analysis.By combining GPS data with air quality measurements, the system creates pollution maps that help identify high pollution areas and trace pollution sources, enabling more focused interventions. Seamless data transmission ensures reliable monitoring, while local backup storage prevents data loss during transmission failures. The system is easily scalable, allowing for the deployment of additional sensors when necessary.Moreover, the system is cost-effective and resource-efficient compared to traditional AQI stations. It requires less physical space and infrastructure, making it suitable for regions with limited resources while still delivering valuable insights into air quality and traffic conditions. This combination of features makes the system a powerful tool for enhancing environmental awareness, public health, and decision-making.
[0017] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0019] FIG. 1 illustrates a mobile air quality monitoring system for estimating, analyzing, and identifying critical pollution zones based on real-time data collection from a mobile sensor networkaccording to some embodiments herein.
[0020] FIG. 2 illustrates the plurality of mdules of the server of FIG. 1 according to some embodiments herein.
[0021] FIG. 3illustrates a feature vector for air quality monitoring using data collected from SDS011 and AHT10 sensors, integrated with GPS dataaccording to some embodiments herein.
[0022] FIG. 4A-Bare flow diagrams that illustrates a methodfor estimating, analyzing, and identifying critical pollution zones based on real-time data collection using a mobile air quality monitoring systemaccording to some embodiments herein.
[0023] FIG. 5 is a schematic diagram of a computer architecture in accordance with the embodiments herein.
DETAILED DESCRIPTIONOF THE DRAWINGS
[0024] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein.The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein.Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0025] As mentioned, there remains aneed to address the aforementioned technical drawbacks in existing technologies in developing a mobile air quality monitoring system that provides an effective and scalable solution for estimating, analyzing, and identifying critical pollution zones.Referring now to the drawings, and more particularly to FIGS. 1 through 5, where similar reference characters denote corresponding features consistently throughout the figures, preferred embodiments are shown.
[0026] FIG. 1 illustrates a mobile air quality monitoring system for estimating, analyzing, and identifying critical pollution zones based on real-time data collection from a mobile sensor networkaccording to some embodiments herein. The system 100 includes a mobile air quality sensing unit 102 that includes a plurality of air quality sensors 104A-N deployed on vehicles. Each sensor is configured to detect particulate matter (PM2.5 and PM10) of air and environmental parameters comprising temperature, humidity, and vehicle speed. The plurality of air quality sensors 104A-N are equipped with a calibration module that periodically adjusts the sensor readings to account for drift and ensure measurement accuracy over time. Aplurality of GPS modules 106A-Nis integrated into each air quality sensor 104A-N configured to collect geographic coordinates comprising latitude and longitude of the plurality of air quality sensors 104A-N at a given moment, and timestamp of when the particulate matter and the environmental parameters were collected at a given location.The system 100 includes a data processing unit 108communicably connected to the plurality of air quality sensors 104A-N. The data processing unit 108is configured to (i) continuously collect air quality data comprising the particulate matter (PM2.5 and PM10) and the environmental parameters and real-time GPS data comprising the GPS coordinates from the plurality of air quality sensors 104A-N, and (ii) transmit the collected data to a server 110 through a communication network 112 for real-time analysis.The communication network 112 may include a GPRS module for data transmission which is integrated with a network status monitoring function that assesses connectivity and signal strength, ensuring optimal data transmission performance and automatic re-transmission in case of failures.The system 100 includes a server110configured toestimate air quality levels at unsampled locations using the collected air quality and GPS data by applying Inverse Distance Weighting (IDW) spatial interpolation to generate an interpolated data. The IDW spatial interpolation includes a dynamic weighting mechanism that adjusts the influence of data points based on real-time sensor reliability and data freshness, thereby improving the estimation accuracy for air quality levels in unsampled areas. The pollution levels are estimated based closer data points having a higher influence. By utilizing the collected PM2.5 and PM10 values, along with their corresponding GPS coordinates (latitude and longitude), the system estimates pollution levels at unsampled locations. The IDW technique operates on the principle that data points closer to an unsampled location exert more influence than distant ones, enabling a more accurate estimation of air quality levels. This process helps visualize the spatial distribution of pollutants across the city, identifying critical areas with higher pollution concentrations and potential exposure risks.The server 110 is configured to process the interpolated data using a machine learning model 114 to (a) analyze air quality patterns, (b) forecast future pollution trends, and (c) identify factors affecting air quality comprising vehicle speed, traffic density, and environmental conditions.The server 110 utilizes machine learning and AI algorithms to process the complete dataset, which includes PM2.5, PM10, temperature, humidity, and location data. These algorithms are used to develop predictive models that can uncover complex air quality patterns. The machine learning model 114 is configured to use time-series analysis to incorporate historical air quality data and predict the future pollution trends with temporal dependencies. The machine learning model 114 identifies unusual patterns or sudden changes in the air quality data, enabling proactive alerts and detailed analysis of potential pollution sources.Through these techniques, the system 100 forecasts future air quality levels, analyze the most influential factors such as vehicle speed, traffic density, and environmental conditions and detect anomalies in pollution patterns. For example, regression models can predict PM levels based on meteorological conditions, while clustering algorithms can identify areas with similar air quality characteristics, further enhancing the system's ability to pinpoint pollution hotspots. The server 110 is configured to identify statistically significant clusters of high particulate matter concentrations across various regions by implementing a hotspot detection model, on the machine learning outputs comprising the analyzed air quality patterns, the forecasted future pollution trends, and the identified factors affecting air quality.
[0027] The server 110 employs hotspot detection algorithms to pinpoint statistically significant clusters of high particulate matter concentrations, building on the machine learning outputs that analyze air quality patterns, forecast future pollution trends, and identify influencing factors. The hotspot detection model 116 comprises a multi-scale analysis feature that identifies pollution hotspots across various spatial resolutions comprising neighborhood, city, and regional scales. Techniques such as the Getis-Ord Gi* statistic or kernel density estimation are applied to the PM2.5 and PM10 data, along with their spatial coordinates, to locate these clusters. These identified hotspots highlight areas with elevated pollution levels that may require targeted interventions or further investigation. By detecting these critical zones, city planners and environmental agencies can effectively prioritize efforts and allocate resources to mitigate pollution in the most impacted areas.The server 110 is configured to detect the critical pollution zones based on the identified clusters of the high particulate matter concentrations and the analyzed air quality patterns, providing actionable insights for targeted interventions and environmental management. The server 110 is configured to combine the real-time GPS with the air quality data to generate pollution maps. The pollutant levels are overlaid on the geographical coordinates to visualize the spatial distribution of air pollution across different regions.
[0028] Table 1 compares air quality datasets based on data collection features.
[0029] Table 1:
[0030] The table 1 compares various air quality datasets based on several data collection features and parameters. It highlights key aspects like the number of data points, the availability of GPS data, and the capability to capture air quality readings at different times of the day (morning and evening), across multiple local areas, and during different seasons. The Dataset obtained by the system 100 stands out with the highest number of data points (261,500) and the most comprehensive data collection features. It includes GPS data, captures readings during morning and evening, spans across multiple local areas, considers seasonal variations, and monitors speed. Additionally, it records temperature, relative humidity, PM2.5, and PM10 parameters.Other datasets such as fedesoriano, Vopani, Chitwan Manchanda, Adarsh Rouniyar, and Seshu Pavan are more limited. They typically lack GPS data, do not capture seasonal or speed-related information, and are restricted to smaller datasets. Most of them record only PM2.5 and PM10 levels, while fedesoriano's dataset captures only PM2.5.
Name Number of Datapoints GPS Data Morning and Evening Capture Local Multi area Capture Seasonal Capture Speed Capture Parameters (Temp, Relative Humidity, PM2.5, PM10)
Dataset of the present invention 261500 Yes Yes Yes Yes Yes Yes
fedesoriano Dataset 36193 No No No No No Only PM2.5
Vopani Dataset 40000 No Yes No No No PM2.5 and PM10
Chitwan Manchanda Dataset 1836 No Yes No No No PM2.5 and PM10
Adarsh Rouniyar Dataset 12240 No Yes No No No PM2.5 and PM10
Seshu Pavan Dataset 23505 No Yes No No No PM2.5 and PM10
[0031] FIG. 2 illustrates the plurality of modules of the server 110of FIG. 1 according to some embodiments herein. The server 110 includes a database 202, a data receiving module 204, a spatial interpolation module 206, the machine learing model 114, the hot spot detection model 116 and a critical zone detection module208.
[0032] The data receiving module 204 is configured to collect air quality data including the particulate matter (PM2.5 and PM10) and the environmental parameters from the plurality of air quality sensors 104A-N deployed on vehicles, and the real-time GPS data including the GPS coordinates from the GPS modules 106A-N integrated into each air quality sensor 104A-N. The spatial interpolation module 206 is configured toestimate air quality levels at unsampled locations using the collected air quality and GPS data by applying Inverse Distance Weighting (IDW) spatial interpolation to generate an interpolated data. The pollution levels are estimated based closer data points having a higher influence. The machine learning model 114processes the interpolated data to (i) analyze air quality patterns, (ii) forecast future pollution trends, and (iii) identify factors affecting air quality comprising vehicle speed, traffic density, and environmental conditions. Statistically significant clusters of high particulate matter concentrations across various regions by implementing the hotspot detection model 116on the machine learning outputs including the analyzed air quality patterns, the forecasted future pollution trends, and the identified factors affecting air quality.The critical zone detection module 208 is configured to detect the critical pollution zones based on the identified clusters of the high particulate matter concentrations and the analyzed air quality patterns, providing actionable insights for targeted interventions and environmental management.
[0033] FIG. 3illustrates a feature vector for air quality monitoring using data collected from SDS011 and AHT10 sensors, integrated with GPS dataaccording to some embodiments herein. The feature vector depicted integrates multiple sensor and GPS-based parameters to offer a comprehensive view of air quality conditions. It includes particulate matter (PM2.5 and PM10) readings from the SDS011 sensor, environmental factors such as temperature and humidity captured by the AHT10 sensor, and geographic information provided by GPS coordinates (latitude, longitude) and speed data. The system also records a timestamp to track when and where the data was collected. Additionally, it calculates the Air Quality Index (AQI) for a specific route (in this case, Narsapur - BVRIT), allowing for the identification of pollution levels in real-time. This combination of data points enables the system to monitor and analyze air quality dynamically and accurately across various locations, supporting environmental assessments and decision-making for interventions.
[0034] FIG. 4A-Bare flow diagramsthat illustratesa methodfor estimating, analyzing, and identifying critical pollution zones based on real-time data collection using a mobile air quality monitoring systemaccording to some embodiments herein.
[0035] At step402, the method includes detecting particulate matter (PM2.5 and PM10) of air and environmental parameters comprising temperature, humidity, and vehicle speed using a mobile air quality sensing unit comprising a plurality of air quality sensors, the mobile air quality sensing unit is deployed on vehicles.
[0036] At step 404, the method includes collecting geographic coordinates comprising latitude and longitude of the air quality sensor at a given moment, and timestamp of when the particulate matter and the environmental parameters were collected at a given location using a GPS module integrated into each air quality sensor.
[0037] At step 406, the method includes continuously collecting air quality data comprising the particulate matter (PM2.5 and PM10) and the environmental parameters and real-time GPS data comprising the GPS coordinates from the plurality of air quality sensors by a data processing unit communicably connected to the plurality of air quality sensors.
[0038] At step 408, the method includes transmitting the collected air quality data and the real-time GPS data to a server via a GPRS module of the mobile air quality sensing unit for real-time analysis.
[0039] At step 410, the method includes estimating air quality levels at unsampled locations using the collected air quality data and the real-time GPS data by applying Inverse Distance Weighting (IDW) spatial interpolation to generate an interpolated data, pollution levels are estimated based on closer data points having a higher influence.
[0040] At step 412, the method includes processing the interpolated data using a machine learning model to analyze air quality patterns, forecast future pollution trends, and identify factors affecting air quality comprising vehicle speed, traffic density, and environmental conditions.
[0041] At step 414, the method includes identifying statistically significant clusters of high particulate matter concentrations across various regions by implementing a hotspot detection model, on the machine learning outputs comprising the analyzed air quality patterns, the forecasted future pollution trends, and the identified factors affecting air quality.
[0042] At step 416, the method includes detecting the critical pollution zones based on the identified clusters of the high particulate matter concentrations and the analyzed air quality patterns, providing actionable insights for targeted interventions and environmental management.
[0043] A representative hardware environment for practicing the embodiments herein is depicted in FIG. 5 with reference to FIGS. 1 through 4. This schematic drawing illustrates a hardware configuration of a central server 110 /computer system in accordance with the embodiments herein. The central server 110 /computer includes at least one processing device 10 and a cryptographic processor 11. The special-purpose CPU 10 and the cryptographic processor (CP) 11 may be interconnected via system bus 14 to various devices such as a random access memory (RAM) 15, read-only memory (ROM) 16, and an input/output (I/O) adapter 17. The I/O adapter 17 can connect to peripheral devices, such as disk units 12 and tape drives 13, or other program storage devices that are readable by the system. The central server 110 / computer can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein. The central server 110/computer system further includes a user interface adapter 20 that connects a keyboard 18, mouse 19, speaker 25, microphone 23, and/or other user interface devices such as a touch screen device (not shown) to the bus 14 to gather user input. Additionally, a communication adapter 21 connects the bus 14 to a data processing network 26, and a display adapter 22 connects the bus 14 to a display device 24, which provides a graphical user interface (GUI) 30 of the output data in accordance with the embodiments herein, or which may be embodied as an output device such as a monitor, printer, or transmitter, for example. Further, a transceiver 27, a signal comparator 28, and a signal converter 29 may be connected with the bus 14 for processing, transmission, receipt, comparison, and conversion of electric or electronic signals.
[0044] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications without departing from the generic concept, and, therefore, such adaptations and modifications should be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims.
, Claims:I/We Claim:

1. A mobile air quality monitoring system (100) for estimating, analyzing, and identifying critical pollution zones based on real-time data collection from a mobile sensor network, the system (100) comprising:
a mobile air quality sensing unit (102), wherein the sensing unit (102) comprises,
a plurality of air quality sensors (104A-N) deployed on vehicles, wherein each sensor (104A-N) is configured to detect particulate matter (PM2.5 and PM10) of air and environmental parameters comprising temperature, humidity, and vehicle speed;
a plurality of GPS modules (106A-N) integrated into each air quality sensor (104A-N), configured to collect geographic coordinates comprising latitude and longitude of the plurality of air quality sensors(104A-N)at a given moment, and timestamp of when the particulate matter and the environmental parameters were collected at a given location;
a data processing unit (108) communicably connected to the plurality of air quality sensors (104A-N), wherein the data processing unit (108) is configured to (i) continuously collect air quality data comprising the particulate matter (PM2.5 and PM10) and the environmental parameters and real-time GPS data comprising the GPS coordinates from the plurality of air quality sensors (6A-N); (ii) transmit the collected data to a server (110) via a GPRS module for real-time analysis; and
a server (110) comprising a processor and a memory, wherein the memory stores instructions that, when executed by the processor, cause the server (110) to:
estimate air quality levels at unsampled locations using the collected air quality and GPS data by applying Inverse Distance Weighting (IDW) spatial interpolation to generate an interpolated data, wherein pollution levels are estimated based closer data points having a higher influence;
process the interpolated data using a machine learning model (114) to (i) analyze air quality patterns, (ii) forecast future pollution trends, and (iii) identify factors affecting air quality comprising vehicle speed, traffic density, and environmental conditions;
identify statistically significant clusters of high particulate matter concentrations across various regions by implementing a hotspot detection model (116), on the machine learning outputs comprising the analyzed air quality patterns, the forecasted future pollution trends, and the identified factors affecting air quality; and
detect the critical pollution zones based on the identified clusters of the high particulate matter concentrations and the analyzed air quality patterns, providing actionable insights for targeted interventions and environmental management.
2. The system (100) of claim 1, wherein the plurality of air quality sensors(104A-N) are equipped with a calibration module that periodically adjusts the sensor readings to account for drift and ensure measurement accuracy over time.
3. The system (100) of claim 1, wherein the IDW spatial interpolation comprises a dynamic weighting mechanism that adjusts the influence of data points based on real-time sensor reliability and data freshness, thereby improving the estimation accuracy for air quality levels in unsampled areas.
4. The system (100) of claim 1, wherein the machine learning model (114) is configured to use time-series analysis to incorporate historical air quality data and predict the future pollution trends with temporal dependencies.
5. The system (100) of claim 1, wherein the hotspot detection model (116) comprises a multi-scale analysis feature that identifies pollution hotspots across various spatial resolutions comprising neighborhood, city, and regional scales.
6. The system (100) of claim 1, wherein the hotspot detection model (116) comprises at least one of Getis-Ord Gi statistic or kernel density estimation to detect the critical pollution zones.
7. The system (100) of claim 1, wherein the GPRS module used for data transmission is integrated with a network status monitoring function that assesses connectivity and signal strength, ensuring optimal data transmission performance and automatic re-transmission in case of failures.
8. The system (100) of claim 1, wherein the machine learning model (114) identifies unusual patterns or sudden changes in the air quality data, enabling proactive alerts and detailed analysis of potential pollution sources.
9. The system (100) of claim 1, wherein the server (110) is configured to combine the real-time GPS with the air quality data to generate pollution maps, wherein pollutant levels are overlaid on the geographical coordinates to visualize the spatial distribution of air pollution across different regions.
10. A method for estimating, analyzing, and identifying critical pollution zones based on real-time data collection using a mobile air quality monitoring system (100), the method comprises:
detecting particulate matter (PM2.5 and PM10) of air and environmental parameters comprising temperature, humidity, and vehicle speed using a mobile air quality sensing unit (102) comprising a plurality of air quality sensors (104A-N), wherein the mobile air quality sensing unit (102) is deployed on vehicles;
collecting geographic coordinates comprising latitude and longitude of theplurality of air quality sensors (104A-N) at a given moment, and timestamp of when the particulate matter and the environmental parameters were collected at a given location using a plurality of GPS modules (106A-N) integrated into each air quality sensor (104A-N);
continuously collecting air quality data comprising the particulate matter (PM2.5 and PM10) and the environmental parameters and real-time GPS data comprising the GPS coordinates from the plurality of air quality sensors (104A-N) by a data processing unit (108) communicably connected to the plurality of air quality sensors (104A-N),
transmitting the collected air quality data and the real-time GPS data to a server (110) via a GPRS module of the mobile air quality sensing unit (102) for real-time analysis;
estimating air quality levels at unsampled locations using the collected air quality data and the real-time GPS data by applying Inverse Distance Weighting (IDW) spatial interpolation to generate an interpolated data, wherein pollution levels are estimated based on closer data points having a higher influence;
processing the interpolated data using a machine learning model (114) to analyze air quality patterns, forecast future pollution trends, and identify factors affecting air quality comprising vehicle speed, traffic density, and environmental conditions;
identifying statistically significant clusters of high particulate matter concentrations across various regions by implementing a hotspot detection model (116), on the machine learning outputs comprising the analyzed air quality patterns, the forecasted future pollution trends, and the identified factors affecting air quality; and
detecting the critical pollution zones based on the identified clusters of the high particulate matter concentrations and the analyzed air quality patterns, providing actionable insights for targeted interventions and environmental management.



Dated this 11thday of October, 2024

Signature of Patent Agent:
(Arjun Karthik Bala)
IN/PA-1021

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