1. Introduction
The problem of air pollution is a significant concern for public health and the environment, and it has gained global attention. Studies have established a connection between inadequate air quality and various health issues, such as respiratory and cardiovascular illnesses. According to the World Health Organization (WHO), air pollution is responsible for approximately 7 million premature deaths per year [1]. In addition to its impacts on human health, air pollution has adverse environmental effects, including climate change, biodiversity loss, and ecosystem degradation [2,3]. Air quality monitoring is critical for understanding the degree of air pollution and its effects on human health and the environment. Real-time air quality monitoring is a relatively new technology that enables the near-real-time collection, processing, and presentation of air quality data [4,5]. This technology provides up-to-date information on air pollution levels, which can help inform decision-making and policy development [6].
Geographical Information System (GIS) mapping technology is critical in real-time air quality monitoring. GIS technology combines geospatial data (e.g., latitude and longitude coordinates) with other information, such as air pollutant measurements and meteorological data, to visually represent air quality in different regions [7,8,9]. This technology may offer precise and reliable information on air pollution levels at various times of the day, assisting in identifying sources of air pollution and evaluating the efficiency of pollution control measures. GIS mapping has several advantages over traditional air quality monitoring methods, such as manual sampling and laboratory analysis. Traditional methods are often time-consuming and expensive and can only provide data for a limited number of locations [10]. In contrast, GIS mapping offers real-time data for multiple sites simultaneously, enabling a more thorough understanding of air quality patterns and changes [11,12]. Additionally, GIS mapping can help identify areas with high pollution levels, which can inform targeted interventions and policies [13].
Implementing GIS mapping for air quality monitoring presents challenges and limitations that require attention. The accuracy relies on sensor quality; faulty sensors can undermine system credibility. Data processing needs efficient techniques due to the high volume from drone sensors. Privacy concerns arise, as data may reveal sensitive information; anonymization and security are crucial [14]. It is essential to address the security aspects of drone communications in implementing GIS mapping for air quality monitoring. Ensuring the secure data transmission from drones to monitoring stations is critical to prevent unauthorized access, data breaches, and potential misuse of sensitive information. Highlighting the importance of strong communication protocols and encryption mechanisms is essential to protect the integrity and confidentiality of the gathered air quality data. Implementing strong security measures for drone communications adds an extra layer of protection, bolstering the overall reliability and trustworthiness of the air quality monitoring system [15]. To harness GIS mapping’s potential, addressing these challenges is vital. Integrating GIS with smart drones and geospatial tech can aid sustainable solid waste dump yard development and environmental management.
The core objective of this research is to employ cutting-edge smart drone technology to collect air quality data surrounding a solid waste dump yard, complemented by geospatial technology to enable real-time analysis of this air quality data. By integrating these innovative techniques, our study aims to comprehensively understand the intricate dynamics governing air quality in the vicinity of the waste dump yard. At its essence, this research seeks to unravel the dual aspects of temporal and spatial variability in gas concentrations. The investigation into spatial variability involves meticulously examining gas concentration fluctuations at various heights. Simultaneously, our study delves into the temporal dimension by scrutinizing the data monthly. By meticulously analyzing these two dimensions, our study provides a comprehensive and insightful perspective on the air quality dynamics surrounding the waste dump yard. In doing so, our research contributes valuable knowledge to environmental monitoring and offers insights that can inform effective management strategies.
Firstly, the study examines the available literature on the use of GIS mapping to monitor air quality levels, highlighting the benefits and limitations of this approach. Secondly, real-time air quality data is collected using drones and IoT built in the drones around the dump yard. Additionally, the paper explores the potential of GIS mapping in predicting air pollution levels and identifying areas of high pollution concentrations. Finally, the article discusses the implications of GIS mapping for air quality management and policy-making. Overall, this paper highlights the potential of GIS mapping technology in improving air quality monitoring and control and the importance of integrating GIS mapping technology into air quality management strategies.
2. Literature Review
Several studies have developed real-time systems for monitoring air quality using wireless sensor networks (WSNs) and GIS mapping to track pollution levels in air [16,17,18,19]. Ref. [20] built a system for real-time monitoring of air quality that employs WSNs and GIS mapping to detect PM2.5 and other contaminants in metropolitan areas. Similarly, [21] developed a system for monitoring air quality using WSNs and GIS mapping to track air pollutant levels in urban cities. Additional research has looked into how to leverage Internet of Things (IoT) devices and GIS mapping to create a system for monitoring air quality in real-time for smart cities. Ref. [22] developed a method for measuring air quality in real-time, especially in smart cities, using IoT devices and GIS mapping to provide real-time data on air pollution levels. Similarly, [23] developed a real-time system for measuring air quality that uses IoT devices and GIS mapping to track air pollution levels in urban areas.
Apart from monitoring air pollution levels, various investigations have explored the possible health consequences of being exposed to elevated pollution levels. Ref. [24] studied the solid waste management of soft drink containers in a multidimensional framework. A particular study revealed that exposure to high levels of PM2.5 was linked to higher mortality rates in urban regions [25]. Another study found that exposure to high levels of NO2 was associated with an increased risk of respiratory diseases in urban areas [26]. Numerous investigations have also investigated the utilization of GIS mapping to simulate and foresee air pollution levels in real-time. For example, [27,28] developed a real-time air pollution prediction model using GIS mapping and machine learning algorithms. Similarly, [29] developed a real-time air quality prediction model using GIS mapping and deep learning techniques. Research was conducted on solid waste management for Malaysia for environmental impact assessment and policy by [30].
Further research has concentrated on constructing mobile air quality monitoring systems by exploiting GIS mapping and other technologies. Ref. [31] developed a mobile air quality monitoring system that uses GIS mapping and machine learning algorithms to monitor air pollution levels in real time. Similarly, [32,33] developed a mobile air quality monitoring system that uses GIS mapping and IoT devices to track air pollution levels in real-time. Numerous investigations have also explored the possibility of involving citizen science in supporting real-time air quality monitoring endeavors. For example, [34] created an air quality monitoring system based on citizen science that utilizes GIS mapping and crowdsourcing to gather and analyze air pollution data. Another research undertaking was carried out to study the meteorological parameters using IoT and drones for the polar regions [35].
Several studies have explored GIS mapping in real-time air quality monitoring [27,36,37]. An instance of this is a study conducted in Beijing, China, which employed GIS mapping to observe the spatial and temporal trends of air contamination within the city [20,38]. The study found that air pollution levels were highest during rush hour and in areas with heavy traffic. Another study in Mexico City employed GIS mapping to evaluate air pollutants’ spatial and temporal fluctuations and their origins. The study indicated that traffic emissions were the city’s foremost cause of air pollution [39]. Another study [40] was conducted for Italy on the optimal regulatory framework for municipal solid waste management.
In general, the utilization of GIS mapping and other technologies to establish real-time air quality monitoring systems can enhance public health and environmental results to a great extent in cities. These systems can provide policymakers and the public with real-time data on air pollution levels, which can inform decision-making and facilitate the development of effective pollution mitigation strategies. Nonetheless, this study utilized this technology to investigate dump sites in urban regions, to acquire a thorough comprehension of the effects of air pollution inside and in the vicinity of these locations, and to create efficient approaches for tackling this urgent matter.
3. Materials and Methods
3.1. IoT Device and Drone Parameters
Air quality monitoring in and around dump yards is essential today, as dumpsites can cause environmental and health hazards [41]. With the introduction of drones, solid waste monitoring can be made more efficient and safer. IoT devices are becoming increasingly popular in various industries, and drones are no exception [42,43,44]. The drones used in this study were equipped with a range of sensors. These sensors allowed us to collect data on various air quality parameters. We also collected data on temperature and humidity, which can impact solid waste accumulation. In this study, we deployed drones with a payload capacity of up to 2 kg and a flight duration of up to 45 min to conduct solid waste monitoring in dumpsites. The drones used in this research have two models, one with four arms and the other with six, which can fly up to a height of 9 to 10 m. The sensor array components are affixed to an unmanned aerial vehicle (UAV) to gauge critical parameters. The UAV is programmed to traverse specified study zones, capturing vital atmospheric data for parameter detection. Subsequently, the collected data is transmitted to cloud servers for storage. However, an initial processing phase is executed before utilization to eliminate disruptive noise and enhance data quality. To address data scarcity, a methodology for imputing missing values is implemented. A Gaussian filter, as proposed by [45], is employed to mitigate noise within measurements. Furthermore, instances lacking data within the dataset are supplemented with positive values to minimize forecast discrepancies. Following pre-processing, these data values are employed as inputs for training within the predictive framework. The UAV’s flight trajectory can be preprogrammed, dictating altitudes and locations for effective data collection. Consequently, the acquired data are wirelessly transmitted to a central station for comprehensive analysis. At this juncture, data are subjected to processing, furnishing invaluable insights into the environmental conditions within the monitored region. This analysis, in turn, facilitates swift and well-informed responses to environmental shifts. This made them suitable for collecting solid waste data in dumpsites, where waste can accumulate in different layers and levels.
3.2. Data Collection Processes
In recent times, the process of gathering data has gained more significance, particularly in the realm of monitoring the environment. The development of drone-based air quality monitoring using wireless sensor networks has revolutionized how we collect data on environmental conditions [46,47]. With the help of drones, it is now possible to monitor air quality in a particular area without human intervention. Equipped with advanced technologies and a variety of sensors, the drone can collect data on several environmental factors, such as air pollution levels, temperature, and humidity. This makes it an incredibly efficient and reliable system for monitoring air quality parameters [48,49]. The drone can be programmed to fly at specific altitudes and locations to collect the required data. The drone wirelessly transmits the collected data to a base station for analysis, where they can be analyzed and processed to provide valuable insights into the environmental conditions of the area being monitored, allowing for quick and informed decisions to be made in response to changes in environmental conditions. Real-time monitoring ensures that data are always up-to-date, enabling prompt action to address any environmental concerns.
When it comes to air quality monitoring, the drone system is equipped with sensors that measure various environmental data such as temperature, humidity, smoke, wind, VOC, propane, toluene, alcohol, LPG, acetone, dust density, CO, CO2, NH4, and H2. Once the drone acquires environmental data, it transmits them to the internet and stores them in a cloud-based system. The drone also transmits the measured time, date, pollution levels, temperature, and other air quality parameters. By incorporating a GPS, the drone can pinpoint the exact location it takes its measurements, providing precise information on the air quality conditions in specific areas.
The drones were flown at different heights (3 m, 6 m, and 9 m) to capture the variation in gas concentration at different levels. The sensors used in this study include the MQ-2 gas sensor for detecting LPG, propane, hydrogen, and methane gases, the MQ-131 and MQ-135 sensors for detecting CO2, NO2, and SO2 gases, and the MQ-7 sensor for detecting CO gas. In addition to gas-detecting sensors, the study also used the GP2Y1010AU0F PM 2.5 sensor for measuring particulate matter levels, the VM 30 sensor for wind direction and speed measurement, and the DHT22 temperature and humidity sensor for measuring temperature and humidity levels. The systematic data collection model is also represented in Figure 1.
These sensors provided a comprehensive picture of air quality conditions in the solid waste dump yard, which is critical for identifying potential health hazards and implementing appropriate mitigation measures [50,51,52]. Our data collection approach involved drones with sensors deployed at varying altitudes of 3 m, 6 m, and 9 m. The study area underwent data collection at regular intervals of 3 months, corresponding to March, June, and September within a single year 2022. This strategic approach allowed us to effectively capture the dynamic variations in air quality that manifest seasonally. These drones were systematically guided along circular flight paths covering the solid waste dump area. During these flights, air quality parameters were recorded at brief intervals, facilitating the acquisition of a comprehensive and dynamic dataset. It is important to note that the data collection intervals were not structured around a quarterly schedule. Instead, data points were collected at intervals of just a few seconds during the circular flight path of the drone. This real-time approach enabled us to capture instantaneous variations in air quality parameters, comprehensively depicting prevailing conditions. Our methodological choices were intentionally designed to account for spatial and temporal variations holistically. Integrating drones at multiple altitudes and adopting continuous data collection effectively addressed spatial resolution nuances and rapid shifts in air quality, resulting in a robust dataset.
3.3. Identification of Study Area and Data Collection
Air quality monitoring in solid waste dump yards is a critical environmental issue due to the release of harmful gases and particles from decomposing waste. The study area for this research was the Kodungaiyur dump yard located in Chennai, Tamil Nadu, as represented in Figure 2. This landfill site is one of the largest and oldest in Chennai, spanning over 212 acres. It receives waste from the entire city of Chennai, generating approximately 2700 metric tons daily. The waste at the site is mainly unsegregated, leading to high environmental pollution and health hazards for workers and nearby residents.
The drone system offers a reliable, efficient, and cost-effective way to monitor air quality parameters and provide real-time data analysis. The sample of data collected is presented in Figure 3 to represent the sample data visually. Its ability to collect data automatically, provide real-time monitoring, and implement on-board pollution abatement solutions makes it an invaluable environmental monitoring and management tool.
3.4. Data Analysis
Using drones and IoT devices to monitor solid waste and air quality has considerable potential to transform the approach we adopt for overseeing and addressing solid waste concerns. One of the main advantages of using drones for solid waste monitoring is the ability to collect data from various levels of the dumpsite. By collecting data from different height levels in the solid waste dumpsite, drones can monitor the waste accumulation at different levels and layers, which is essential for effective waste management.
In addition to data collection, drones can also be used to create 3D models of the dumpsite, which can help identify areas with high levels of waste accumulation, allowing for more targeted interventions to improve solid waste management. Furthermore, the ability of drones to collect data in real-time using sensors and IoT devices is a significant advantage over traditional monitoring techniques, which often involve manual data collection and analysis. Real-time data collection and transmission allow more timely and accurate responses to solid waste issues. When drones are deployed at various elevations, the corresponding data for each altitude are documented, and based on these data, air quality maps for the study area can be generated for both current and long-term ecological analyses. This map illustrates the air quality conditions prevailing in the region, facilitating improved monitoring and management of environmental conditions. Drones are deployed to gather environmental data and air quality parameters for various pollutants in the study area at different heights above the landfill. After transmitting environmental data to the station, software will be employed to generate maps illustrating the air quality. In this research, the geospatial mapping process was facilitated by utilizing the open-source Quantum Geographic Information System (QGIS) software, version 3.22.14. Employing IDW interpolation techniques in conjunction with data gathered from drone-based surveys along predetermined flight paths, the study aimed to produce comprehensive air pollution maps. The QGIS software was a versatile platform for generating detailed visualizations of air pollutant concentrations. Specifically, the study focused on several pollutants detected by the drone across different altitudes and distinct months (March, June, and September). By amalgamating data from multiple sources, the maps depicted pollutant levels at various elevations within each respective month’s designated landfill site area. Moreover, the synthesized information sheds light on the spatial and temporal variations in pollutant concentrations. This robust geospatial approach allowed for a thorough assessment of air pollution emanating from landfill sites and offered a nuanced understanding of how pollution levels fluctuate across altitude and time dimensions, presenting a comprehensive framework for comprehensive environmental evaluations. Table 1 effectively illustrates all of the mentioned variables in the simulation.
4. Results and Discussions
Using drones in data collection has been a game changer in many fields, and environmental studies are no exception. In the above conversation, we see how a drone was deployed to collect data on air quality parameters such as temperature, humidity, CO, CO2, NH4, propane, VOC, wind speed, and smoke at various altitudes ranging from 3 m to 9 m. The drone was programmed to fly at different altitudes, and as the altitude increased, the coverage of the drone also increased. In the case of air quality studies, drones have proven to be especially useful, as they can collect data on a wide range of parameters in real-time. This enables us to quickly identify areas experiencing high levels of pollution and take appropriate action to mitigate the situation.
The experimental results of the study on air pollutant parameters were analyzed for different months of the year, with a focus on March. The findings, illustrated in Figure 4a–i, present the measurement data plot for various parameters such as temperature (in C), humidity (in %), CO (in PPM), CO2 (in PPM), NH4 (in PPM), propane (in PPM), VOC (in PPM), wind (in MPH), and smoke (in PPM) at different altitudes. Moreover, Table 1 encompasses the factual information and the variance projected by the interpolation model. The actual data acquired from sensors mounted on drones, which were averaged from diverse elevations, are exhibited in Table 1. These outcomes clearly indicate that the density of air pollutants differs substantially with altitude. The levels of pollutants such as CO, CO2, NH4, propane, VOC, wind, and smoke were observed to be higher at lower altitudes closer to the solid waste, whereas higher altitudes had comparatively lower concentrations. This highlights the impact of solid waste on the environment and the need for proper waste management practices.
Table 2 displays the outcomes of measuring air pollutant parameters at varying elevations near a landfill site. The measurements were taken at 3 m, 6 m, and 9 m altitudes, and the values obtained are tabulated in the rows of Table 1. Looking at the first parameter, temperature (Figure 4a), it is observed that the values recorded at each altitude are within the same range, with a maximum difference of only 2.1 degrees Celsius between the lowest and highest altitudes. This suggests that temperature is relatively constant at these different altitudes in this region. Humidity values show a slight variation within the same range (Figure 4b), with the highest value recorded at the 6 m altitude and the lowest at the 9 m altitude. This can be attributed to the fact that humidity is affected by the temperature and the altitude. The temperature at the 6 m altitude may have been conducive to higher humidity levels. In contrast, the altitude of 9 m may have resulted in lower humidity due to the decreased atmospheric pressure at higher altitudes.
The levels of CO and CO2, which are harmful pollutants, are seen to decrease as altitude increases (Figure 4c,d). The highest levels of CO and CO2 are recorded at the lowest altitude of 3 m, with CO values of 45.0 ppm and CO2 values of 9.2 ppm. These values exceed the recommended safe limits for both pollutants [53]. The levels of NH4, propane, and VOC, which are also harmful pollutants, are observed to increase as altitude decreases, with the highest levels recorded at the 3 m altitude (Figure 4e–g). This suggests that these pollutants impact the immediate vicinity of the solid waste dump. The highest concentration recorded for NH4 is 58.8 ppm, significantly higher than the safe limits. Similarly, the highest propane concentration is recorded at 6.3 ppm, higher than the safe limits. VOC levels are relatively low at all altitudes, with the highest concentration at 1.8 ppm. Wind speed is also measured at each altitude, and the values recorded are seen to increase with altitude (Figure 4h). The highest wind speed is recorded at the 9 m altitude, indicating that the upper atmosphere is more turbulent than the lower atmosphere. This can help to disperse pollutants and reduce their concentrations.
Finally, smoke levels are observed to decrease as altitude increases (Figure 4i). This is likely because smoke particles are heavier than air and tend to settle closer to the ground. The highest concentration of smoke is recorded at the 3 m altitude, which is closest to the solid waste dump. Table 2 furnishes valuable insights into the dispersion of air pollutants around a landfill at diverse elevations. The results indicate that the levels of pollutants vary significantly with altitude, with the highest concentrations of pollutants recorded at the lowest altitude. This suggests that the immediate vicinity of the solid waste dump is most impacted by air pollution from the landfill. The results also indicate that wind speed and altitude can help disperse pollutants and reduce their concentration. All of these variations are plotted as a graphical representation for a clear understanding in Figure 5.
The data collected by the drone in the conversation above are an excellent example of the power of drones in environmental studies. The drone data provide a good picture of the environment, which is impossible with data collected from ground-based sensors. The data can also be used to create heat maps showing areas with the highest pollution levels. This data can subsequently be utilized to formulate specific tactics to enhance the air quality in these regions.
Table 3 exhibits the measurements of distinct air pollutant parameters taken at different elevations of 3 m, 6 m, and 9 m above the surface in June. These measurements were carried out at the exact location as the previous data collection, but with a 3-month interval to compare and observe any potential changes. Furthermore, the number of parameters measured was increased to enhance the study’s efficacy. The parameters include temperature, humidity, CO, CO2, NH4, propane, acetone, toluene, heat index, LPG, alcohol, H2, and dust density. These parameters are crucial indicators of air quality and can significantly impact human health and the environment.
From Table 3, it can be observed that the temperature increases as the height increases, with the highest temperature of 34.5 °C recorded at the highest point of 9 m (Figure 6a). The humidity decreases as the height increases, with the lowest value of 55.4% recorded at the highest point (Figure 6b). This shows that the high humidity in municipal solid waste depicts the high humidity and lower temperature near the waste. CO, CO2, NH4, propane, acetone, toluene, and LPG show a decreasing trend as the height increases (Figure 6c–h,j). The highest values of these parameters are recorded at the lowest point of 3 m. This suggests that these pollutants are more concentrated near the ground.
On the other hand, the parameter of heat index shows an increasing trend with height (Figure 6i). The maximum heat index, a gauge of the perceived temperature incorporating both humidity and actual temperature, is documented at 9 m. Similarly, the highest values of alcohol and H2 are recorded at 3 m (Figure 6k,l), while the highest dust density is recorded at 6 m (Figure 6m). This indicates that the concentration of this pollutant is higher at a moderate height from the ground. In summary, Figure 7 shows that air pollutant parameters vary significantly with altitude. These findings can facilitate comprehension of the dispersion of air pollutants in the atmosphere and support the development of productive strategies to curtail their levels.
Table 4 provides air pollutant data measured at three different heights of 3 m, 6 m, and 9 m in September, 3 months later than the previous measurements. The data recorded include temperature, humidity, CO, CO2, NH4, ethanol, acetone, toluene, and heat index. As observed from the table, the temperature ranged from 36.2 °C to 37.8 °C at all three heights (Figure 8a), while the humidity reached its highest level of 72.7% at an altitude of 9 m (Figure 8b). The concentrations of CO2, CO, and NH4 were lower at 9 m compared to other heights (Figure 8c–e). The concentrations of ethanol and acetone were highest at a height of 3 m (Figure 8f,g), and the concentration of toluene was higher at 3 m and 6 m compared to 9 m (Figure 8h).
The heat index, a measure of the human-perceived temperature, reached its highest level of 56.9 °C at a height of 3 m (Figure 8i). The increase in the heat index can be attributed to the presence of pollutants like ethanol, acetone, and toluene, which are often found in industrial areas. However, the overall air quality seemed to improve with increasing height as the concentration of CO and NH4 decreased at higher altitudes. Overall, the data suggest that air pollution levels decrease with altitude, with the lowest levels observed at 9 m. Nonetheless, the distinct patterns for individual pollutants differ, as illustrated in Figure 9, suggesting that various factors could impact their dispersion in the atmosphere. Notably, the information demonstrated in Table 4 was amassed through sensors mounted on drones, which encompassed a limited radius at 3 m above ground level and progressively broadened as the altitude heightened. The data collected from these sensors can help develop effective measures to control air pollution in urban areas. These results could be helpful for policymakers and environmental researchers to develop strategies to mitigate air pollution.
Within this segment, we showcase the outcomes of air pollutant monitoring at diverse elevations throughout March, June, and September. Table 5 compares the temperature, humidity, CO, CO2, and NH4 values at different altitudes (3 m, 6 m, and 9 m) during three selected months (March, June, and September). Overall, the data have some similarities and differences across the three months and three altitudes.
In terms of temperature, there is a general increase from March to June, followed by a slight decrease in September. At each altitude, the temperature is highest in September. The humidity values show a similar trend, dropping from March to June and slightly increasing in September. At 6 m and 9 m, the humidity is significantly higher in September compared to the other two months. The levels of CO and CO2 are highest at 3 m in March and June and then decrease at 6 m and 9 m. In September, the levels of both gases are generally lower compared to the other two months, with the lowest levels at 9m. NH4 levels follow a similar pattern, with the highest levels at 3 m in March and June, and then decreasing at higher altitudes. In September, the NH4 levels are lowest at 9 m.
These data suggest that temperature, humidity, and pollutant levels can vary significantly with altitude and time of year. Understanding these patterns, represented in Figure 10, can help inform strategies for mitigating air pollution and improving air quality. Emphasizing the implementation of results, this outlines strategies for improving air quality management in landfill areas. The proposed collaboration with local environmental agencies, extended data collection periods, health impact assessments, simulation modeling, community engagement, interdisciplinary collaboration, and policy advocacy collectively offer a robust framework for addressing the complex issue of air pollution around landfill sites.
A critical and imperative pathway for future research is the meticulous sensor accuracy assessment. This involves conducting an extensive and rigorous comparative analysis between parameters observed at airborne altitudes and those at ground level. By accomplishing this, the integrity and dependability of the sensors can be definitively ascertained. Additionally, a thorough investigation is warranted into the influential factors, notably the impact of propeller dynamics and key meteorological variables like humidity and wind speed. This prospective inquiry holds the explicit potential to enhance the precision and authenticity of the collected data and make a substantial contribution to the nuanced understanding of the intricate air quality dynamics within landfill regions. By immersing into these facets, the forthcoming research endeavors can promise exceptionally refined insights, fortifying the comprehensive efficacy of environmental assessments and formulating adept management strategies tailored for landfill areas.
5. Conclusions
In conclusion, our research paper presents a novel approach for air quality monitoring and analysis of solid waste dump yards using drone and IoT technology and geospatial mapping. The study involved collecting and analyzing data on various air quality parameters, including temperature, humidity, and pollutant concentrations such as CO, CO2, NH4, and toluene. Our analysis found that air pollutant levels varied significantly based on factors such as altitude, proximity to solid waste, and humidity. For instance, pollutant levels such as CO, NH4, propane, and smoke were found to be significantly higher at lower altitudes and closer to solid waste. CO was in the highest range of 45 ppm in March, which is equivalent to CO released indoors in poorly ventilated areas with malfunctioning or improperly vented combustion appliances. NH4 reached its highest value, of 66.7 ppm, in September. In contrast, parameters such as temperature and humidity slightly varied with altitude.
The spatial interpolation for air quality parameters using QGIS software allowed us to create accurate geospatial maps of the study region, supplying a thorough comprehension of the scope and dispersion of air pollution within and near the landfill. Our analysis revealed that the Kodungaiyur dump yard had high environmental pollution and health hazards caused by the unsegregated waste, with pollutant concentrations well above the permissible limits. Furthermore, our study demonstrated the potential benefits of real-time air quality monitoring using GIS mapping technology. GIS mapping can offer precise and current data on air pollution rates, thus empowering decision-making and policy formulation to enhance air quality and safeguard the public’s well-being. Utilizing this state-of-the-art technology at landfills in metropolitan areas marks substantial progress in comprehending the harmful consequences of air pollution on the nearby surroundings. While integrating GIS mapping technology offers tremendous potential for enhancing air quality management, several challenges and considerations must be addressed. Overcoming technical, infrastructure, data privacy, stakeholder engagement, and scaling challenges will be pivotal in effectively integrating GIS mapping into existing air quality management frameworks. By proactively addressing these challenges, stakeholders can harness the full potential of GIS mapping to improve decision-making and pave the way for more sustainable and informed air quality management strategies.
Our study emphasizes the pressing requirement for efficient measures and actions to alleviate air pollution’s adverse effects on human well-being and the natural environment. By leveraging the latest technology and data-driven approaches, we can create a cleaner, healthier, and more sustainable future for future generations.
R.H.R.: Conceptualization, Methodology, Writing—Original draft preparation, Validation, Editing; S.B.: Data analysis, Manuscript editing, Software; P.P.: Conceptualization, Methodology, Manuscript editing; M.S.: Conceptualization, Data Collection and Methodology; C.M.: Data Collection and Analysis; V.R.: Data Collection and Manuscript editing. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Due to confidentiality agreements, supporting data can only be available to bonafide researchers subject to a non-disclosure agreement.
The authors wish to acknowledge the Ministry of Environment, Forest, and Climate Change, Government of India, for providing funding for this research work. Further, the authors want to recognize the Greater Chennai Corporation, Chennai, Tamilnadu, India, for permitting us to collect data for this research.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 8. Variations in Air Quality Parameters at Different Altitudes in September.
Enlarge this image.Figure 8. Variations in Air Quality Parameters at Different Altitudes in September.
Enlarge this image.Figure 10. Pattern of Variation in Air Quality Parameters across Various Months and Altitudes.
Simulation Parameters.
| Parameter | Details | |
|---|---|---|
| Drone Specifications | Payload Capacity: up to 2 kg | |
| Flight Duration: up to 45 min | ||
| Drone Models | Four-arm model | |
| Six-arm model | ||
| Data Collection Approach | Preprogrammed flight trajectory with altitudes and locations | |
| Data Collection Intervals | Real-time collection at intervals of a few seconds during circular drone flight paths across different altitudes | |
| Altitude Range | 3 m, 6 m, 9 m | |
| Sensor Array | Affixed to UAV for measuring critical parameters | |
| Measured Parameters and Their Sensor Types | SENSORS | PARAMETERS |
| MQ-131 | LPG | |
| Propane | ||
| Hydrogen | ||
| Methane gases | ||
| MQ-135 | CO2 | |
| NO2 | ||
| SO2 | ||
| MQ-7 | CO gas | |
| VM 30 | Wind direction | |
| Speed measurement | ||
| DHT22 | Temperature | |
| Humidity sensor | Humidity | |
| Noise Mitigation | Gaussian filter employed to mitigate noise within measurements | |
| Geospatial Mapping | QGIS software | |
| Mapping Techniques | IDW interpolation techniques used for generating air pollution maps | |
| Input for Predictive Framework | Processed data values used for training within the predictive framework | |
| Focus of Study | Pollutants detected across different altitudes (3 m, 6 m,9 m) and months (March, June, September) | |
Height-Dependent Air Quality Data for March.
| Altitude | Temperature (°C) | Humidity (%) | CO (PPM) | CO2 (PPM) | NH4 (PPM) | Propane (PPM) | VOC (PPM) | Wind (MPH) | Smoke (PPM) |
|---|---|---|---|---|---|---|---|---|---|
| 3 m | 32.6 | 63.4 | 45.0 | 9.2 | 58.8 | 6.3 | 449.2 | 4.0 | 640.0 |
| 6 m | 34.0 | 62.8 | 40.3 | 8.3 | 42.4 | 4.6 | 449.6 | 3.7 | 540.3 |
| 9 m | 34.7 | 63.0 | 39.6 | 7.7 | 32.8 | 3.8 | 449.3 | 3.6 | 439.6 |
Height-Dependent Air Quality Data for June.
| Altitude | Temperature (°C) | Humidity (%) | CO (PPM) | CO2 (PPM) | NH4 (PPM) | Propane (PPM) | Acetone (PPM) | Toluene (PPM) | Heat Index (°C) | LPG (PPM) | Alcohol (PPM) | H2 (PPM) | Dust Density (ug/m3) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 m | 31.7 | 58.2 | 40.9 | 9.0 | 53.1 | 8.0 | 45.0 | 24.4 | 37.1 | 50.4 | 18.5 | 12.2 | 963.0 |
| 6 m | 33.5 | 55.8 | 33.4 | 8.5 | 41.3 | 4.1 | 28.1 | 12.6 | 39.1 | 40.0 | 6.9 | 6.2 | 993.7 |
| 9 m | 34.5 | 55.4 | 25.5 | 7.3 | 21.4 | 3.5 | 13.0 | 9.3 | 39.3 | 35.6 | 5.6 | 5.3 | 881.2 |
Height-Dependent Air Quality Data for September.
| Altitude | Temperature (°C) | Humidity (%) | CO (PPM) | CO2 (PPM) | NH4 (PPM) | Ethanol (PPM) | Acetone (PPM) | Toluene (PPM) | Heat Index (°C) |
|---|---|---|---|---|---|---|---|---|---|
| 3 m | 37.8 | 64.5 | 36.5 | 8.9 | 66.7 | 237.1 | 53.0 | 21.6 | 56.9 |
| 6 m | 36.7 | 72.5 | 28.3 | 8.7 | 50.9 | 184.7 | 27.2 | 14.3 | 58.5 |
| 9 m | 36.2 | 72.7 | 19.3 | 7.3 | 37.4 | 102.0 | 14.6 | 7.4 | 56.7 |
Comparison of Air Quality Data for Different Months and Different Altitudes.
| Month | March | June | September | ||||||
|---|---|---|---|---|---|---|---|---|---|
| 3 m | 6 m | 9 m | 3 m | 6 m | 9 m | 3 m | 6 m | 9 m | |
| Temperature (C) | 32.6 | 34 | 34.7 | 31.7 | 33.5 | 34.5 | 37.8 | 36.7 | 36.2 |
| Humidity (%) | 63.4 | 62.8 | 63 | 58.2 | 55.8 | 55.4 | 64.5 | 72.5 | 72.7 |
| CO (PPM) | 45 | 40.3 | 39.6 | 42.9 | 34.4 | 25.5 | 36.5 | 28.3 | 19.3 |
| CO2 (PPM) | 9.2 | 8.3 | 7.7 | 9.0 | 8.5 | 7.3 | 8.9 | 8.7 | 7.3 |
| NH4 (PPM) | 58.8 | 42.4 | 32.8 | 53.1 | 41.3 | 21.4 | 66.7 | 50.9 | 37.4 |
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; Balusamy, Shanthini 2 ; Partheeban, Pachaivannan 3
; Mani, Charumathy 2 ; Madhavan Sridhar 3
; Rajasekaran, Vinodhini 4 1 Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, Chennai 600054, Tamilnadu, India
2 Department of Information Technology, St. Peter’s Institute of Higher Education and Research, Chennai 600054, Tamilnadu, India;
3 Department of Civil Engineering, Chennai Institute of Technology, Kundrathur, Chennai 600069, Tamilnadu, India;
4 Department of Electronics and Communication Engineering, S. A. Engineering College, Chennai 600077, Tamilnadu, India;