Content area
Background
There is a rising issue of pollution in the atmosphere. Particulate matter, CO, CO2, NO, NO2, ozone etc. are causing adverse impacts to the surrounding and the well-being of every individual living across the entire globe. Therefore, there is a stringent need to be aware of these atmospheric pollutants for safeguarding the quality of air that we human being breathe in.
Materials
In this work, we integrate microcontrollers like Arduino Uno and Raspberry Pi along with the primary measuring sensors like ozone sensor and PM7003 sensor for carrying out characterization of air quality and variability investigation of several traits/parameters by including but not limited to temperature, pressure, humidity, PM1, PM2.5, PM10, and ozone. By deploying Arduino Uno, Raspberry Pi and other miscellaneous components, we developed an interactive and effective method for collecting practical data corresponding to the quality of air.
Results and discussion
First, we present our experimental set-up along with its observations made using the set-up for the several parameters considered for measuring the air quality. Then, we present the graphical comparative study of traits/parameters like temperature, pressure, humidity, PM1, PM2.5, PM10, and ozone. Then, we present the summary of observations made for the major pollutants for characterization of air quality and variability investigation. Then, we carry out the case study using three different types of sensors like temperature, MQ2, and MQ4 sensors as an extension of the major segment of our work. Finally, the performance validation of measurement was done using four common performance indicators.
Article Highlights
The article highlights are presented in the following three pointers:
By using two microcontrollers in our design, the air quality monitoring endeavors by us are of much reliable, accomplishing proper balance between the data acquisition and complex data handling.
With insights derived out of this work, better understanding of the relationship between the climatic/ environmental conditions and hazard-causing pollutants is made possible.
With the better quantification of air quality contaminants through our work, several air-borne diseases can be prevented by adopting counter measures to maintain human’s health condition.
Introduction
Rapid growth in industry and traffic expansion have increased societal and governmental focus on air pollution in emerging nations. One of the much-recognized consequences of prolonged air pollution contact is the onset of severe breathing-related ailments [1]. Administrations might encounter a burden of substantial cost if the state of the air keeps getting worse. Air quality detection set-ups are an important medium for preventing the future worsening of pollutants in the air [2]. Conventional stations for tracing air quality are not suited for widespread installation in densely populated regions such as towns, due to its corresponding size and expense [3].
Environmental surveillance techniques are used for a variety of purposes, including air pollution management [4, 5–6], predicting weather [7, 8], evaluation of agricultural damages [9, 10], and water quality management and tracing [9, 11, 12] using simpler embedded systems [13]. The goal is to provide beneficial environmental circumstances for farming, humans, or any other species on the planet [14]. The Internet of Things (IoT) and wireless technologies have simplified and automated surveillance of environmental scenarios.
Background
Because of the high amounts of pollutants that are harmful to human health, particularly in big metropolitan areas, there has been an increased necessity to monitor air quality factors during the past ten years [15]. Due to this fact, several kinds of monitoring systems have been developed, and these systems are now regarded as essential to the execution of pollution mitigation measures. To give authorities and the public vital information about the present concentration of gases and particles in various parts of the city, these systems are designed to monitor air quality factors [16].
Moreover, lengthy offline procedures are needed even when precise measurement results are achievable. Consequently, real-time information on air quality cannot be obtained using this approach [2]. This work focuses on high geographical and temporal resolution data on air quality, which are generally needed [17]. Air quality can now be measured and related data may be transmitted to servers via wireless networks such as WSNs (i.e., Wireless Sensor Networks) thanks to the rapid advancement of IoT (i.e., Internet-of-Things) technology [18, 19].
Motivation
The conventional approach of measuring air quality involves a collection of costly, sturdy stations that need to be calibrated and maintained on-site. Data with inadequate spatial resolution typically result from a low number of monitoring stations due to their expensive cost [20, 21]. However, in recent years, new technology paradigms like the IoT have also led to the creation of monitoring systems, making it possible to install a greater number of air quality monitoring systems [22]. To gather and analyze information globally for decision-making and action in a particular context, a framework of computers known as IoT enables communication and transmission of data amongst heterogeneous items (distinctively recognizable) [23]. Because low-cost sensors are used in IoT-dependent air quality monitoring systems, numerous sensor systems with reduced related costs may be developed, and the collected data can be accessed permanently and in real time [16].
Problem statement
Individuals with respiratory problems and a history of asthma are especially vulnerable to pollutants in the air [24, 25–26]. Therefore, administrating and managing the quality of air are essential [17, 26, 27–28]. To estimate the extent of pollution in the atmosphere, the Air Quality Index (AQI) has been employed globally to determine the relationship between different airborne pollutants by appropriate quantifications [29, 30]. The AQI may be considered as a medium for presenting individuals with simple and observable facts (for instance, it reveals atmospheric pollution levels). Because numerous regions of the nation have different AQIs based on region-wise air-quality norms, it is necessary to quantify, monitor, and manage the total volume of PM (which encompasses minute particles such as PM2.5 and PM10) as well as elements such as nitrogen dioxide (NO2), Ozone, carbon monoxide (CO), and sulfur dioxide (SO2). Fine PM (referring to specifically PM2.5) is a particle content which forms one of the major causes of environmental pollution, and this is associated with harmful medical repercussions of various levels of exposure [31]. PM2.5 has also been identified as one of the main causes of fatalities from lung-related illnesses spanning from the acute conditions to the chronic conditions [32] caused due to various actions like ignition of fuel. It is also held responsible for triggering mortality rates [33]. Thus, multiple research initiatives, notably [34], carried out investigations in this area considering harmful effects of PM (also considering PM1) that affects the climatic situations and the health of humanity. These investigations were able to reveal links between temporary health outcomes [35] and PMs- PM2.5 and PM10 [36], which motivated the researchers of the diverse fields to choose PM as one of the parameters to include while quantifying air quality.
According to [37], ozone is considered a supplementary pollutant, which is produced by complicated nonlinear processes involving 2 distinct kinds of indicators: nitrogen oxides along with volatile organic molecules [38, 39]. Just like PM, it is also held responsible for influencing climatic conditions to a considerable extent. Ozone is an important component of the photochemical cycle and is tied to the meteorological chemical characteristics. Many air quality concerns are there globally due to the presence of ozone and its remains, posing serious unhealthy issues like decrease of lifespan, risk to heart-related and lung-related diseases, higher level of mortality [40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56–57], etc.
For governing the contaminant levels in the air (like Ozone, NO2, PM1, PM2.5, and PM10), many standards for atmospheric air quality and subsequent emission prevention measures came into practice all around the globe (e.g., [58, 59, 60, 61–62]) through the establishment of constraints and target standards, prospective goals, info restrictions, and alarm thresholds for safeguarding the well-being of humans [63]. For quantifying and implementing such standards and emission prevention measures, the measurement of such air pollutants has now become indispensable. This ensures to maintain the standards of air quality and lower the pollutants in the atmosphere. Furthermore, some of the atmospheric traits like temperature, pressure, and humidity play a critical role in tracing environmental conditions like observing levels of pollutants and managing air quality [64]. Therefore, such traits additionally need to be measured, quantified, and interpreted.
Objectives
The objectives of the research study concerned with the characterization of air quality and variability investigation of several parameters have been presented in the following pointers:
To use and integrate the prospects of 2 microcontrollers, namely, Arduino Uno and Raspberry Pi.
To use the primary measuring sensors like ozone sensor and PM7003 sensor to measure particulate matter and ozone contents in the atmosphere.
To additionally investigate the air quality prospects through the usage of supplementary sensors like temperature, MQ2, and MQ4 sensors.
To develop an interactive and effective method for collecting practical data corresponding to the quality of air by successful integration of all.
To enable continual monitoring of all atmospheric traits and air quality parameters using appropriate sensors.
To comprehensively investigate the performance prospects of our advanced air quality monitoring framework.
Paper outline
The remaining sections of the research work is organized as follows: Sect. 2 provides with the literature survey of recent research papers published related to the air quality monitoring and contains the tabulation that compares the state-of-the-art environmental and air quality monitoring research works. Sect.ion 3 comprise the major challenges that are normally posed to air qualities. Section 4 offers the proposed methodology of Air Quality and Variability Investigation through various sections such as air quality parameters, block diagram, components, integration operation and major functionalities. Section 5 discusses results of the proposed methodology through observations, graphical representations, and comparison tables. Finally, Sect. 6 gives an overall conclusion to the research work through offering remarks regarding the research methodology.
Literature survey
In the literature, several investigators have attempted to control and trace the air quality. Some of the state-of-the-art environment and air quality monitoring works attempting to trace particulate matter, CO, CO2, NO, NO2, ozone etc. have been picked and discussed in this section.
Contemporary environment and air quality detecting works
In this part, we will review the contemporary environment and air quality detecting works that not only characterizes major air quality parameters like particulate matter, CO, CO2, NO, NO2, etc., but also characterizes ozone [37], one of the supplementary pollutants in the atmosphere.
Firstly, we review the contemporary environment and air quality detecting works characterizing multitude of air quality parameters excluding ozone.
[27] proposed an innovative lowered air quality tracing system to improve the failure recognition (i.e., process of identifying faults or failures) of the structure indulging the tracing of quality of air. On the contrary to traditional Kernel Partial Least Squares (KPLS), the proposed approach improved the failure recognition of an air quality tracing structure by means of calculation duration and rate of false alarms by only employing the essential latent elements.
[65] utilized an Integrated Contact Reaction framework to predict early deaths across more than twenty-nine towns in India during the year 2016. Average fatality was used to assess the early death rate associated with PM2.5 intake. They assessed and sorted these early fatalities according to the health issues related to brain, lungs, and heart by concentrating on the ages of the considered group of individuals.
[66] studied how people were exposed to levels of PM2.5 in a metropolitan area. During the span of June month and October month on 2015, PM tracing operations were conducted in a major metropolitan region.
[67] described an inexpensive facility for tracking pollution. This facility functioned as just one node in a network that was wireless. Every node used by them captured pollutants from the air and weather information on a regular basis and uploaded them to a centralized free-to-use server.
[68] performed extended-duration experimentation at outdoors on the Nanjing location between December month of the year 2015 and May month of the year 2017 to test the abilities of internally manufactured inexpensive PM trackers employing the Shinyei PPD42NS detector suitable for atmospheric PM2.5 measurement. The utilized approach produced an excellent estimate of hour-wise PM2.5 by means of two performance metrics.
[2] suggested a novel strategy for developing a framework for monitoring air quality by making use of advanced IoT. The information obtained by adaptable devices was sent practically over a broad-area network operating with reduced power. The IoT was used to evaluate and appraise every piece of information relevant to air pollution.
[69] suggested a relatively inexpensive practically feasible IoT framework for mini and micro solar production facilities capable of monitoring continual current, continual voltage, 7 climatic factors, and fluctuating power. The suggested technology analyzed every essential environmental factor and collected solar production info straight from the power station (rather than via the inverters).
[70] offered an affordable practically feasible IoT structure for air quality tracing. Gas-form contaminants were monitored using detectors measuring CO and air quality. Furthermore, the structure used a wireless networking module-backed Arduino Nano developmental platform to efficiently communicate measurements to a ThingSpeak internet communication medium for immediate and practical visualization of quality in air.
[71] presented an assessment of the power utilization of 2 affordable air quality tracing frameworks, wherein one of the frameworks was constructed upon a Raspberry Pi platform and another framework was constructed upon an Arduino platform. The suggested air safety solutions used ready-to-use electronics and were simple to install and manage.
[72] aimed to give info utilizing wirelessly operated sensor tech, including the multitude of devices/ platforms like Zigbee, Arduino Nano, Raspberry Pi, wireless sensor networks, and sensory devices. The Graphical User Interface (GUI) used by this work displayed the results of the information collected by sensory devices using integrated Raspbian Linux. The entire structure was built with open-sourced tech platforms like the Zigbee Pi and Raspberry, both of which were economical and had minimal power usage. The sensory devices collected information concerning several conditions in the environment and sent it to the platform- Raspberry Pi, which functions as the foundation platform. A few sensory devices process information straight away and transmit it to the Raspberry Pi, whereas other ones transmit information onto the Raspberry Pi via Arduino Nano’s serial connection.
[73] considered the fundamental advantage of the IoT in detecting the state of the air, which was the capacity to communicate information across a network with no need for human–computer or human-to-human contact. They presented air quality tracking utilizing MQ135 and DHT11 sensory devices, Raspberry Pi, and Arduino Uno for identifying CO2, CO gases, and alcoholic presence practically while also displaying web-reliant info such as humidity in the air and thermal values.
[74] presented a 3-level approach for tracing pollution in the atmosphere. They attempted to combine gas detectors, a module for wireless connectivity, and the Arduino Integrated Development Environment (IDE) to create an IoT package. This equipment may be practically deployed in several localities for observing pollution in the atmosphere.
The San Joaquin Valley (SJV), an agricultural and environmental justice (EJ) region with consistently low air quality, was considered as the subject of [75]. With significant nitrate mass levels, the SJV was classified as a major nonattainment region for PM2.5 NAAQS (i.e., National Ambient Air Quality Standards).
[76] The incidence of adult smokers in 1650 Zip codes throughout the Coast states of California, Oregon, and Washington was compared to indoor and outdoor air quality values recorded over almost five years between 2017 and 2021 by PurpleAir, the most extensive network of economical devices in the USA.
Now, we review the contemporary environment and air quality detecting works characterizing ozone- one of the air quality parameters.
[77] developed an intelligent personalized air quality tracing structure for actual surveillance of quality in the air. Their intelligent personalized air quality tracing structure combined readily available inexpensive PM, Ozone, NO2, CO, humidity, and temperature detectors, alongside a global positioning system and a microprocessor. This designed structure was adjusted in the testing facility and checked with measurements taken outdoors.
The purpose of this study [78] was to evaluate the performance of Alphasense’s B4-series inexpensive gas detection devices for detecting Ozone, NO2, NO, and CO under various temperature and relative humidity conditions. The influence of various characteristics on inexpensive gas detection devices was measured in a controlled environment, and an amendment method was developed that was subsequently applied to natural information.
[79] looked into several adjusting frameworks for the actual economical multiple sensing device packages for the pollutant, monitoring CO2, Ozone, NO2, and CO. They investigated 3 approaches, namely, 2 variants of linear regressions and machine learning-oriented adjusting frameworks with random forests. The random forest frameworks equaled or considerably surpassed the other three adjusting frameworks, and their precision and accuracy were stable throughout assessment intervals of as long as sixteen weeks.
[80] examined temporal patterns and spatial deviations in ozone and PM2.5 levels in the state of North Carolina between 2002 and 2016, as well as relationships with the factors of the surrounding area. The merged Air Quality at the outer layer utilizing the Downscaling repository was utilized to obtain calculated everyday ozone and PM2.5 levels for 2010 Census sections, which were summed up to provide section-wise yearly ozone and PM2.5.
[81] analyzed the influence of Vienna’s inaugural shutdown on the surface levels of cumulative oxidants, ozone, and NO2. Frameworks were additionally created to normalize airborne contaminant data over time, allowing for more efficient mediated evaluations.
[82] analyzed sea wind circumstances within the vicinity of Boston and the impact they have had on the levels of regional air contaminants during the last ten years. Breezes in the sea exert a significant impact on the dispersion of main and supplementary contaminants in the urbanized region within Boston.
[83] employed susceptibility investigation methods to investigate the efficiency of comprehensive industrial release reduction initiatives which were widely implemented everywhere across China. Air quality and the synergistic health consequences of NO2 and ozone were researched in order to get a better knowledge of ozone regulation, particularly in a Volatile Organic Compound-restricted environment. When compared to the Pearl River Delta economic area, the yearly median levels of NO2 in Hong Kong were found to be substantially decreased.
[84] examined the spatiotemporal fluctuation in the environmental presence of ozone, PM2.5, and PM10 across Barranquilla, Colombia, between March 2018 and June 2019 using 3 different measuring sites. The findings revealed temporal and spatial variability across the measurement sites and contaminants tested.
Over the years, many works have already started to use advanced sensing mechanisms (like optical and electrochemical) considering the challenges posed by the low-cost sensor technologies. For instance, [85] and [86] came up with such promising air quality monitoring solutions by tackling the challenges posed by low-cost sensor solutions by leveraging the sophisticated measurement of PM, ozone, and other contributing air pollutants. Despite the fact that low-cost sensor technologies are subjected to several challenges like gas cross-sensitivity and zero-level stability, it can still be made reliable, and made to yield measurements with appreciable accuracy by adopting a few counter strategies. These counter strategies (similar to those used by [86]), for instance, calibration approaches are often included in the low-cost sensor monitoring solutions for enabling its resultant measurements to be reliable and accurate to help reduce the environment and health-related consequences.
Comparison of reviewed works
In this part, we are providing the summary of all the discussed state-of-the-art environment and air quality monitoring works in the form of tabulation in below Table 1.
Table 1. Comparison of contemporary environment and air quality detecting works
S.No | Work | Taken air quality parameter (i.e., Pollutants) | Major focus |
|---|---|---|---|
1 | [27] | NO2 and Ozone | Failure recognition process in the air quality tracing system |
2 | [65] | PM2.5 | Early fatalities owing to the abnormalities in brain, lungs, and heart |
3 | [66] | PM2.5 | Individual-specific risks to contaminant exposure in different time slots |
4 | [67] | NO2,CO, PM1, PM2.5, and PM10 | Inexpensive solution for tracing multiple air quality parameters |
5 | [68] | PM2.5 | Extended-duration Outdoor experimentation in Nanjing with inexpensive PM measuring solutions |
6 | [2] | PM2.5 and PM10 | IoT-based monitoring solution with capability to observe alterations in the quality of the air |
7 | [69] | - (Only renewable eneregy production influential factors were taken including climatic factors) | Optimized usage of renewable usage of renewable eneregy resources through practical monitoring of influential factors |
8 | [70] | Contaminants of air includeing CO levels | Air quality tracing and visual alerting with ThingSpeak and IoT to trace bad environmental conditions |
9 | [71] | - (2 platforms-based air quality tracing frameworks were only compared for power-related performance) | Comparison of power prospects of 2 platforms-based air quality tracing frameworks |
10 | [72] | Smoke, Benzene, CO, CO2, and NH3 | Concurrent usage of Arduino Nano and Raspberry Pi alongside Zigbee for tracing several conditions in the environment |
11 | [73] | CO2, CO, and alcoholic presence | Concurrent usage of Arduino Uno and Raspberry Pi by deploying IoT setting for tracing air state |
12 | [74] | Methance, CO, and other air quality parameters | Location-wise pollution status update as per the location changes from time to time |
13 | [77] | PM, Ozone, NO2, and CO | Investigation of air quality in Chennai city during different motion scenarios |
14 | [78] | Ozone, NO2, NO, and CO | Incorporation of cost affordable gas sensing solution |
15 | [79] | CO2, Ozone, NO2, and CO | Economical aspect of multiple sensing device packages concerning USA- Pittsburgh |
16 | [80] | Ozone and PM2.5 levels | Temporal patterns and spatial deviations across the state of North Carolina during considered time frame |
17 | [81] | Oxidants (cumulative), ozone, and NO2 | Assessed lockdown-associated air quality problems |
18 | [82] | Ozone and NO2 | Influence between the sea wind circumstances and air contaminants by investigating the past years in the vicinity of Boston |
19 | [83] | NO2 and ozone | Air quality-specific investigations across volatile organic compound-restricted environment |
20 | [84] | Ozone, PM2.5, and PM10 | Temporal and spatial variability investigations to be aware of urban air quality |
Though numerous studies have recently been completed on constrained air pollution tracing, extensive research is still necessary to increase the preciseness, functionalities, and portability [87]. Hence, enhancements are required to precisely know the levels of major contaminants like PM1, PM2.5, PM10, and ozone particles alongside environmental traits [64] by integrating several embedded devices in the literature [13].
Research landscape
Recent years have witnessed several advancements in the field of air quality studies, including:
The ability to assess air quality has increased thanks to new technology, such as affordable sensors and others.
The capacity to anticipate and predict air quality has improved.
The studies have advanced our knowledge of the relationships between the environment and climate of a certain location, city, or nation and air quality.
It is evident that advancements have been made in the evaluation and control of exposure to different air contaminants and air quality.
Major challenges posed to air qualities
A few typical and unusual difficulties have occasionally been identified from the standpoint of air quality monitoring. While there are differences across applications and no common obstacles in the research published for all purposes involving air quality monitoring systems, the following are the main challenges noted [14]: As per [16], the primary obstacles of the ones documented in the previous literatures have been examined:
Choosing the best technique for sensor measurement presents complications. Uncertainties will surround the values we measure if we choose the wrong measuring technique.
Newer difficulties about its trust levels are sometimes brought about by novel methodologies provided by various air quality monitoring systems.
There could be some difficulties when the systems’ stations are constructed at a lower cost and used for more extensive air quality monitoring.
Analyzing noisy data may be difficult. The information obtained by sensors that are employed for different reasons contains noise. Various external and internal sources might be contributing to the noise.
Regardless of the type of data being monitored and controlled, the goal of the monitoring, or the types of sensors being used, there is currently no viable machine learning technique that can be utilized to solve environmental concerns.
Excessive levels of air pollutants, including particulate matter (PM), ozone (O3), oxides of sulfur (SOX), nitrogen oxides (NOX), and carbon monoxide (CO), have been linked to several serious health issues, including respiratory disorders and even death [88, 89, 90–91].
Other environmental problems brought on by air pollution include the destruction of building structures, acid rain, global warming, ozone layer depletion, and decreased plant and agricultural yields [92, 93–94]. he issue is becoming worse every day in emerging nations [95]. Worldwide, at different levels (national, state/city, and municipal), intensive UAQMP (i.e., Urban Air Quality Management Plans) are being created and put into action to address the worrisome levels of air pollution [88, 95].
In India, there is mounting proof for the injurious consequences caused by the air pollution in human health. Air pollution is responsible for one in eight fatalities in India, corresponding to an article published by the ICMR (i.e., Indian Council of Medical Research). India has conducted studies that demonstrate the relationship between disease load and mortality and both short- and long-term exposure [96].
Proposed characterization of air quality and variability investigation
For characterizing air quality and performing variability investigation of several parameters in the atmosphere, we are integrating several core components like Arduino Uno microcontroller and Raspberry Pi microcontroller along with the primary sensors like ozone sensor and PM7003 sensor. Both the ozone sensor and PM7003 sensor are linked onto the Arduino Uno microcontroller, interfacingit to each sensor. Additionally, the inclusion of analog-to-digital converter is made to facilitate the conversion of analog info provided by the ozone sensor. The major concentration is given to the measurement of PM1, PM2.5, PM10, and ozone particles, which is the major segment of our work (shown in the below Fig. 1) concerned with the characterization of air quality and variability investigation of several parameters.
Fig. 1 [Images not available. See PDF.]
Block diagram of the proposed methodology characterizing air quality and variability investigation
Furthermore, we investigate certain atmospheric traits like pressure, temperature, and humidity. As an extension, we are additionally investigating the air quality prospects through the usage of supplementary sensors like temperature, MQ2, and MQ4 sensors. For measuring these atmospheric traits, we are using the following sensors:
DHT11 sensor helps measure the atmospheric temperature and humidity values. In this sensor, there is a thermistor to detect temperature changes and translates these into an electrical signal and capacitive humidity sensor to measure changes in humidity levels, converting them into an electrical signal.
BMP180 sensor helps measure the atmospheric pressure, using the piezoelectric sensor to detect changes in atmospheric pressure, which is then converted into a digital signal.
OX-A431 sensor helps measure the concentrations of ozone.
MQ2 and MQ4 sensors are used to measure CO, CO2, etc.
The applicable sensors are connected to the Arduino Uno using its digital or analog input pins. The Arduino reads data from these sensors and processes it as needed. Furthermore, OX-A431, MQ2, and MQ4 sensors produce analog outputs, which corresponds to the concentration of concentrations detected. We have used an ADC (i.e., MCP3008), connected to the Raspberry Pi via the SPI interface. This setup converts the analog signals from the used sensors into digital data, which is then read and processed by the Raspberry Pi.
Considered air quality parameters
Let us now discuss the air quality parameters considered in this work concerned with the characterization of air quality and variability investigation of several parameters.
Particulate matter
The total quantity of a diverse range of particulate matter, namely, PM2.5 and PM10 is more important than additional airborne contaminants, according to the investigations demonstrating greater correlations between the quantity of fine particulate matter and negative health-related impacts [97, 98].
As a result, a deeper particulate matter tracing is required whenever analyzing levels of airborne pollutants across an area and duration better to assess origin and composition-reliant air pollution-related health consequences [97].
PM1
To lessen the negative effects on health caused by PMs, emphasis must be given to smaller and extremely small particles [99]. Continued atmospheric pollution observation and epidemiological analyses are necessary to confirm the necessity for PM1-oriented air quality management and legislation [97].
Atmospheric PM1 contributes considerably to the concentration of PM2.5 in the atmosphere we live. Nevertheless, because of a scarcity of ground-oriented PM1 observations from pollution monitoring sites [100], limited has been discovered about the health impacts of PM1 globally. PM1 and PM2.5 were substantially related to higher admissions for pneumonia and Chronic Obstructive Pulmonary ailments, however, they do not influence the rates of infection of the upper respiratory tract or asthma. PM1, a crucial part of PM2.5, is receiving increased academic attention since new data suggests that lower PM portions may be more detrimental to people [101, 102–103]. Nevertheless, present PM1-health investigations are exceedingly rare worldwide because of a shortage of ground-oriented PM1 observations through air surveillance sites [100].
PM2.5
Despite advances in many nations, pollutants in the air, particularly finer particulate matter (i.e.,PM2.5), continue to be a worldwide health issue, resulting in around six million early pulmonary and cardiac fatalities each year. According to [97], they are the result of pollutants in the air both across the exterior and interior. There is a requirement to go further than just controlling PM2.5concentrations in volume across centralized sight units.
Several statistical investigations (for instance, [104, 105, 106, 107, 108, 109–110]) and 2 case–control research investigations [107, 111] reported post the year 2014 assessed the morbidity and mortality associated with prolonged duration PM2.5 exposure [112]. According to [112], collective projections of the relationship between cardiac attacks and PM2.5 contact. Prolonged duration of contact with PM2.5 at ten μg/m3 levels elevated the hazard of cardiac-oriented mortality and morbidity. Contact with PM2.5 at a concentration of ten μg/m3 raised the hazard of pulmonary morbidity and mortality considerably.
PM10
PM10 refers to contaminants in the air that are 10 µm or smaller diameters. PM10 poses elevated risk of heart attack [113], asthma [114], tumour development, and breathing difficulties [115]. Just like PM10, NO2 can emit both naturally occurring and artificial sources. The combustion of petroleum-based products, and the releases from automobiles, power stations, manufacturing facilities, and rough terrain machinery, contribute to atmospheric NO2 concentrations, being recognized as a breathing irritating substance [115, 116].
The levels of PM10 are prone to vary, for instance, PM10 can be different for 2 sessions in a day, namely, (1) during the noon session and (2) post the noon session, demonstrating a collective traffic trends of PM10 during both the afternoon and morning sessions irrespective of raised level of traffic existence at the noon [117, 118]. Furthermore, growing levels of PM10 could be influenced by variables like resuscitation of particles through the deterioration of road surface, wear on brakes, and wear and tear of tyres [119], but not through vehicular engine emissions. Furthermore, concentrations of PM10 can be less owing to minimal traffic in both summer and winter, which resulted in a drop in the rates of emission, as well as due to advantageous distribution circumstances. Ultimately, the concentrations of PM10 are prone to change each hour across both weekends and weekdays [118].
Ozone
Ozone is an active oxidant which has been accountable for over 4 lakh fatalities prematurely and over 80 lakh hospitalizations worldwide annually [40, 43, 52]. Ozone impacts the health of both the heart and lungs. Numerous fatalities are caused by breathing-related conditions, particularly in individuals who suffer from both the asthma and chronic obstructive pulmonary disease. Its atmospheric presence damages plants as well as decreases ecological production [120].
Most anthropogenic liberations are the major source of ground-level ozone, a very reactive and oxidative gas that is frequently seen in suburban and urban areas [51]. Exposure to this pollutant has been linked to unfavorable health consequences, such as an increase in short-term mortality and morbidity, according to a number of epidemiological studies and evaluations by health and environmental organizations worldwide [46, 121, 122–123]. Research on the health effects of ozone exposure is crucial since global warming is expected to raise ozone levels, which has significant implications for climate change research. Numerous multilocation time series investigations in the nations like Latin America, USA, Asia, Europe, and Canada have extensively evaluated short-term ozone mortality connections [123, 124, 125–126].
Effective public health initiatives, such as the establishment, evaluation, and revision of air quality regulations, can be greatly aided by the quantification of health costs associated with air pollution [51]. Few nations now adhere to the more stringent WHO (i.e., World Health Organization) recommendation for air quality, and current regulations vary widely throughout them [127]. By comparing the health impacts of ozone levels above various air quality criteria, it is possible to get important insights into the potential advantages of enhancing present clean air legislation for public health [51]. When it comes to its impacts on plant life, materials, and human health (such as the pulmonary and cardiovascular systems), ground-level ozone is regarded as one of the most dangerous air pollutants as it is a highly reactive gas [57, 63, 128, 129, 130, 131, 132–133].
Block diagram and basic operation
In this work, validation tests are performed in controlled situations with known amounts of pollutants so that probable errors are assessed and known beforehand to yield out accurate outcomes using the utilized sensors. The Raspberry Pi logs and stores the sensor data that is gathered. The data is processed and analyzed using Python programs. Size fractions of PM1, PM2.5, and PM10 are used to classify PM concentrations. Temperature and regional fluctuations of ozone concentrations are measured and analyzed. Utilizing internet access, processed data is sent to a specified web server. During data transfer, data integrity is guaranteed by secure communication methods. To study Air Quality and Variability, a web-based dashboard is created. With the help of this dashboard, users can now monitor remotely and view data more readily.
The block diagram of the proposed methodology is represented in the above Fig. 1. The power supply, cloud server, LCD screen, Arduino Uno, PMS7003 sensor, Ozone sensor, and Analog-to-Digital Converter are connected to the Raspberry Pi. Using the power supply, the Raspberry pi is powered. Then the PM1, PM2.5, and PM 10 readings are sensed through the PMS7003 sensor. Similarly, the ozone gas reading is sensed through the ozone sensor. The sensed readings are converted digitally through analog-to-digital converter and displayed in the LCD screen.
Major components
The components shown in the block diagram of the proposed system are separately briefed on in this section.
Arduino UNO
Our approach is built upon Arduino as its core component. Well-known for its intuitive hardware and software, Arduino is an open-source hardware platform. Our computer is equipped with an IDE (i.e., Integrated Development Environment) that permits one to create and upload code to a physical programmable circuit board, often known as a microcontroller. Because of its adaptability, Arduino has become quite popular among those who are new to electronics. Button, LEDs, motors, speakers, GPS units, cameras, the internet, cellphones, and televisions are just a few of the devices it can connect with. Because it offers interesting hands-on concepts, the Arduino Uno is a dynamic tool for electronics lovers to begin. Understanding about sensors, measurements, and quick prototyping is made easier by its introduction to the unique Arduino experience.
16 × 2 LCD
An electronic screen module with a broad range of uses is the LCD (Liquid Crystal Display) screen. Of them, the 16 × 2 LCD is very notable as a basic element widely used in a wide range of electrical devices and circuits. These modules are probably chosen over seven-segment displays and other multi-segment LED displays for a number of reasons, including their affordability, ease of programming, and unrestricted ability to show unique characters, bespoke animations, and more. Its capability to show sixteen entities every line over 2 lines where each entity denoted by a 5 × 7 pixel matrix is indicated by the phrase “16 × 2”. Two registers make up this type of LCD: the Data register holds the ASCII values of the characters that will be revealed via the LCD, and the Command register holds instructions given to the LCD, such as initializing it, clearing the display, and controlling display functions.
Raspberry Pi
In our arrangement, the Raspberry Pi serves as a base station. Because it runs on the raspbian Linux operating system, the credit-sized Raspberry Pi microprocessor is designed to provide simpler and more reasonably priced wireless monitoring solutions [134].
The ARM11 microprocessor that powers the Raspberry Pi operates at 700 MHz clock frequency. The Raspberry Pi Model B, which utilizes the System on a Chip (Soc) BCM2835, was utilized [135]. Because of its affordable price, low power consumption, SoC with a CPU, GPU, and many other features, this board is an excellent option for system implementation. With their direct connection to the board, the sensor nodes with digital output can instantly supply the required data. Through the use of an analog-to-digital converter, the Raspberry Pi is linked to the ozone sensor.
Power supply
The Raspberry Pi may be powered in a number of ways. For most applications, the common charging unit provided by the platform of Raspberry Pi is advised. With the aid of a DC-DC step-down converting circuitry, the Raspberry Pi may also be powered by the majority of USB power banks and 12 V batteries. Because AGM and lithium-ion batteries are smaller and can be placed in any direction, they are better than conventional lead-iron batteries. The Raspberry Pi can also be powered by solar energy [136, 137].
PMS7003 sensor
There are also many different kinds of sensors for measuring particulate particles. The concept of operation, which records light dispersed by particles transported in an stream of air by a beam of light, is shared by all commercial PM sensors. Due to their relatively low manufacturing costs, these optical sensors range in price from tens to hundreds of US dollars. Furthermore, PM sensors are user-friendly and frequently equipped with a microcomputer interface. For this reason, citizen scientists frequently embrace them for usage.
Multiple aspects of PM sensors were assessed. Priority one was to examine the sensors’ operational stability. For measuring systems intended for long-term monitoring, this feature is very crucial. The primary factor contributing to sensor aging in low-cost PM sensors may be the buildup of particles inside the measurement chamber. Furthermore, harsh weather conditions like high humidity or low temperatures might interfere with electronics’ ability to perform. When utilized outside, however, one must exercise caution because many sensor devices are specifically designed for indoor situations.
Second, the repeatability (or so-called intramodel variability) of the sensor accuracy was assessed across units within the same sensor model. Due to the fact that they do not need to be calibrated individually, devices with high repeatability need smaller calibration efforts.
Mostly for PM2.5 and PM10 fractions, the digital output data from PMS7003 sensors have a mass concentration format. For six size bins, this sensor yields the number of particles per unit volume. The data sheets for each of the evaluated low-cost sensors did not provide information on the factory calibration processes.
Ozone sensor
To measure ozone concentrations, ozone gas sensors usually use electrochemical or semiconductor technology. Ozone sensors have been created using a variety of analytical methods, including colorimetry, conductivity and amperometry-based sensors, photoacoustic spectroscopy, and UV and MIR absorption spectroscopy. The LLOD of the OX-A431 ozone sensor is 15 ppb. This sensor weighs 5 g and has a cylindrical form of Φ20.2 cm × 16.5 cm.
Ozone and a detecting electrode interact chemically in electrochemical sensors to produce an electrical signal that is proportionate to the quantity of ozone. The way that semiconductor materials change electrically when exposed to ozone is the basis for semiconductor sensors. These sensors are essential for keeping an eye on the quality of the air, particularly in places like industrial zones or locations with large machinery that are vulnerable to high ozone levels. In interior settings, they are also employed in ozone generators and air purifiers to maintain acceptable ozone levels.
Cloud server
Using an Internet connection, the cloud informatic system automatically aggregates sensor data and presents it to researchers and physicians via a web-based interface, creating a data communication channel between the sensors and the researchers and clinicians. When many sensors are installed, this technology is very useful in reducing the effort associated with data collecting. A web server for data visualization and a database are the two main components of the cloud system. Time-series plots, bar charts, histograms, and other graphing tools are used by the data visualization server to present the measurements made by the sensors in real-time with past data that has been collected and stored by the database. Furthermore, the web-based user interface enables the download of raw data for additional analysis to a local computer.
Additionally, the sensor unit can synchronize data to its local storage from the cloud server for future feedback, control, and bidirectional communication. The sensor unit may operate independently in the absence of the Internet by locally storing data on an 8 GB external SD card, which is plenty for continuous use.
Integration operation and major functionalities
We are integrating two microcontrollers, namely, Arduino Uno and Raspberry Pi as measuring equipment, creating an interactive and effective method for collecting practical data corresponding to the quality of air. This tech integration allows for continual tracing over several spots, which aids in the evaluation of spatial changes and time patterns. Moreover, the use of these kinds of microcontrollers allows for remote data gathering and investigation, encouraging a thorough knowledge of fluctuations in the quality of air. By observing the variations in the major pollutants like PM1, PM2.5, PM10, and ozone readings, the present research is attempting to reveal the complicated linkages across these airborne contaminants and gain knowledge about the way they interact.
The use of both Arduino and Raspberry Pi in our design was intended to capitalize on the strengths of each microcontroller: Arduino for efficient real-time sensor data acquisition and Raspberry Pi for handling complex data processing and system integration. This combination optimized performance and offered greater flexibility in the system, without much consideration being given to the simplification of the design.
Both the microcontrollers like Arduino Uno and Raspberry Pi and two sensors used have certain functionalities, which are as follows:
Arduino Uno Responsible for processing of multitude of sensor data and transmission of the subsequent data to the and Raspberry Pi (In our work, the Raspberry Pi and Arduino Uno holds a serial linkage between them).
Raspberry Pi (In other words, Central Hub) Responsible for the data logging, processing the logged data, and transmission of the processed data from the concerned integrated sensors.
Ozone Sensor Responsible for the measurement of atmospheric ozone levels.
PM7003 Sensor Responsible for the measurement of several particulates like PM1, PM2.5, and PM10.
Furthermore, there are other components that are being integrated in this work. The functionalities of those components are as follows:
Power supply Responsible for powering up the Central Hub (i.e.) Raspberry Pi.
Analog-to-digital converter Responsible for the conversion of analog info measured and reported by the ozone sensor across the area of interest.
LCD Responsible for displaying the measured and reported measurements from sensor for further processing and interpreting.
Cloud server (based on ThingSpeak) Responsible for generating the graphs for correlating several indulged parameters to obtain insightful associations for air quality.
The present work demonstrates an intricate framework for tracing critical air quality metrics by integrating the functionalities of the Arduino Uno, Raspberry Pi, ozone sensor, and PMS7003 sensor alongside the analog-to-digital converter. The off-site transmission of information capability encourages informed choice-making and enables preventive interventions to combat pollution in the atmosphere. This initiative adds to the larger goal of supporting better living conditions. The installed internet-based server (ThingSpeak) promotes an improved understanding of the concentrations of ozone and particulate matter, raising consciousness regarding issues related to the environment (responsible for the degradation of air quality), which has become possible by integrating data from two major sensors.
Counter strategies adopted for low-cost solution
The techniques used to address the issues of cross-sensitivity, zero-level errors, and humidity influence in our sensor measurements are presented below.
For mitigating cross-sensitivity, we have done following procedures:
Calibration techniques To reduce cross-sensitivity, we performed calibration of each sensor with specific target gases in controlled environments. Calibration curves were developed to account for cross-sensitivity effects and to accurately determine the concentration of the target gas.
Sensor fusion algorithms We used sensor fusion techniques, such as weighted averaging or Kalman filtering, to combine data from multiple sensors and minimize the impact of cross-sensitivity on the final readings.
For addressing zero-level errors, we have done following procedures:
Baseline correction Zero-level errors were mitigated through baseline correction techniques. We regularly performed zero calibration in the absence of target gases to account for any sensor drift or offset.
Data normalization Raw sensor data were normalized against a known baseline to correct for zero-level errors before further analysis.
Validations and calibrations of sensors
For the sensor calibration and validation methods used in our study. Here is a comprehensive explanation of our calibration and validation procedures.
The sensors were calibrated by exposing them to known concentrations of target gases in a controlled environment. We used a series of calibration gases with accurately known concentrations to establish calibration curves for each sensor. These curves relate the analog signal outputs to the corresponding gas concentrations.
Sensors were initially validated in a laboratory setting where they were exposed to standard gas mixtures. We used a gas calibration system to generate precise concentrations of the target gases.
For further validation, sensors were deployed in parallel with reference instruments (i.e., chromatographs) in an environmental monitoring setup.
The calibration curves were generated using regression analysis of the sensor outputs against known gas concentrations. We applied linear or polynomial fitting, as appropriate, to obtain conversion equations that were then used to estimate gas concentrations from sensor readings during the experiments.
Conversions in our design
The process of converting the analog signals from the applicable sensors (OX-A431, MQ2, and MQ4) into meaningful gas concentration values involves several steps, including calibration, mathematical conversion, and implementation.
After establishing the calibration curves, mathematical equations are derived to convert the raw sensor signals into gas concentrations. This involves:
Calibration Equations The relationship between the sensor’s analog output and the gas concentration is often linear or polynomial. The calibration equations are determined through regression analysis of the calibration data. For example:
The used Linear Calibration Equation is as follows:
C = a⋅V + bC = a \cdot V + bC = a⋅V + b
Here,
CCC is the gas concentration.
VVV is the analog voltage output from the sensor.
aaa and bbb are coefficients determined through regression analysis.
The used Polynomial Calibration Equation is as follows:
C = an⋅Vn + an − 1⋅Vn − 1 + ⋯ + a1⋅V + a0C = a_n \cdot V^n + a_{n-1} \cdot V^{n-1} + \cdots + a_1 \cdot V + a_0C = an⋅Vn + an − 1⋅Vn − 1 + ⋯ + a1⋅V + a0
Here,
CCC is the gas concentration.
VVV is the analog output.
an,an − 1,…,a1,a0a_n, a_{n-1}, \ldots, a_1, a_0an,an − 1,…,a1,a0 are coefficients obtained from polynomial regression analysis.
Mean Absolute Error (MAE)
Mean Bias Error (MBE)
Root Mean Squared Error (RMSE)
Coefficient of Determination (R2.)
Result and analysis
The collected info from the sensory devices and associated tools is the basis for the suggested system’s outcomes. Robust data validation techniques are incorporated into the program by the suggested methodology to detect anomalies or improbable sensor values. This might use threshold comparisons, range checks, or statistical techniques for anomaly detection.
Experimental set-up and its observations
To document occurrences of anomalous or inaccurate sensor readings, the proposed effort built up an extensive error recording system. Logging can help with problem solving and system enhancement. The images below display the experimental setup photos together with the corresponding readings of the proposed system linked to an LCD screen via a Raspberry Pi.
The real- time hardware setup of the proposed methodology characterizing air quality and variability investigation is depicted in the above Fig. 2. The elements are connected with one another in the same manner as shown in the above Fig. 1.
Fig. 2 [Images not available. See PDF.]
Image showing the complete setup of the proposed methodology characterizing air quality and variability investigation
At a particular sample rate, PMS7003 offers real-time measurements and updates. The PMS7003 sensor measures the following: PM1, PM2.5, and PM10, and shows the respective readings on the LCD screen.
The particulate matter particles whose size is less than 1 micron is referred as PM1. The respective PM1 size is displayed as 8.0 in the LCD screen as shown in above Fig. 3.
Fig. 3 [Images not available. See PDF.]
Image showing the PM1 reading in the LCD screen
The particulate matter particles whose size is less than 2.5 micron is referred as PM2.5. The respective PM2.5 size is displayed as 12.0 in the LCD screen as displayed in above Fig. 4.
Fig. 4 [Images not available. See PDF.]
Image showing the PM2.5 reading in the LCD screen
The particulate matter particles whose size is less than 10 micron is referred as PM10. The respective PM10 size is displayed as 12.0 in the LCD screen as denoted in above Fig. 5.
Fig. 5 [Images not available. See PDF.]
Image showing the PM10 reading in the LCD screen
An inorganic molecule with a strong fragrance that is light blue and in gas form is called ozone. The respective ozone reading is displayed as 233 in the LCD screen as expressed in above Fig. 6.
Fig. 6 [Images not available. See PDF.]
Image showing the Ozone reading in the LCD screen
Graph-based comparisons for characterization of air quality
Comparison of major pollutants
The graph plotted in the above Fig. 7 depicts the concentration of PM1 (particulate matter smaller than 1 micron) across a period of 2 weeks. At the start, PM1 levels are elevated, influenced by sources such as industrial operations and vehicle exhaust. Following a short period of fluctuation, there is a consistent decrease in PM1 levels. This trend suggests the effectiveness of pollution control strategies, including tighter emission standards and enhanced air quality initiatives, leading to a notable enhancement in air quality over time.
Fig. 7 [Images not available. See PDF.]
PM1 reading comparison in various dates
The chart depicted in the above Fig. 8 depicts the fluctuation in PM2.5 (particulate matter with a diameter smaller than 2.5 microns) concentration across a specified duration of 2 weeks. Time is represented on the horizontal axis while the vertical axis measures PM2.5 concentration in micrograms per cubic meter (µg/m3). Initially, there is a notable surge in PM2.5 levels, likely attributable to industrial discharges, vehicular exhaust, and other human-induced activities. Minor oscillations occur early in the timeline, possibly influenced by daily variations or weather patterns. Subsequently, a consistent downtrend in PM2.5 concentration is observed, indicating the effectiveness of pollution mitigation strategies. These strategies may involve stricter emission controls, improved air quality management techniques, and reductions in industrial and vehicular emissions. Towards the conclusion of the observation period, a significant decrease in PM2.5 levels is evident, signaling a marked enhancement.
Fig. 8 [Images not available. See PDF.]
PM2.5 reading comparison in various dates
The graph plotted in the above Fig. 9 illustrates the variation in PM10 (particulate matter less than 10 microns) levels across a time span of 2 weeks. At the outset, there is an increase, probably stemming from emissions from industries and vehicles. Small deviations are observed, potentially influenced by daily shifts or weather conditions. Following this, there is a steady decline, suggesting the success of pollution control efforts such as enhanced emission regulations. Finally, there is a notable decrease, signaling an enhancement in air quality.
Fig. 9 [Images not available. See PDF.]
PM10 reading comparison in various dates
The graph plotted in the above Fig. 10 illustrates ozone concentrations measured in parts per billion (ppb), with the x-axis showing the dates spanning across 2 weeks duration, and the y-axis indicating ozone levels from 0 to above 500 ppb. A gradual decline in ozone levels is evident over the period. This downward trend may be attributed to seasonal variations, emission changes, or the success of pollution control measures. Analyzing these trends is essential for evaluating air quality and formulating effective environmental policies.
Fig. 10 [Images not available. See PDF.]
Ozone reading comparison in various dates
Later, the PM2.5, PM10 and ozone readings are recorded for 24 h and is represented as a comparative graphical representation in the aqi.in server.
The PM2.5 readings have been recorded for the period of 24 h and have been represented as a comparative graphical representation as given in the above Fig. 11.
Fig. 11 [Images not available. See PDF.]
PM2.5 readings for 24 h
The PM10 readings have been recorded for the period of 24 h and have been represented as a comparative graphical representation as presented in the above Fig. 12.
Fig. 12 [Images not available. See PDF.]
PM10 readings for 24 h
The ozone readings have been recorded for the period of 24 h and have been represented as a comparative graphical representation as seen in the above Fig. 13.
Fig. 13 [Images not available. See PDF.]
Ozone readings for 24 h
Comparison of miscellaneous parameters
Here, every other miscellaneous parameter such as the temperature, pressure and humidity readings taken during various dates are plotted in the form of comparison graphs through Thingspeak server.
The x-axis denotes the dates between 2 weeks’ time duration, while the y-axis indicates the temperature measurements. The chart depicted in the above Fig. 14 shows a steady rise in temperature, peaking at 32 degrees Celsius. This increase may be due to seasonal shifts, climate trends, or specific weather conditions in the area. Analyzing these temperature changes is crucial for understanding climate behavior, forecasting weather, and making informed choices in fields like agriculture, energy usage, and environmental policy.
Fig. 14 [Images not available. See PDF.]
Temperature reading comparison in various dates
The x-axis displays the dates between 2 weeks’ time duration, while the y-axis indicates the pressure values. The chart depicted in the above Fig. 15 demonstrates a steady rise in pressure, surpassing 99,000 Pascals. This visual representation helps in comprehending trends affected by atmospheric conditions and weather patterns, which are crucial for meteorological studies and weather predictions.
Fig. 15 [Images not available. See PDF.]
Pressure reading comparison in various dates
The x-axis depicts the dates between 2 weeks’ time duration, and the y-axis displays the humidity values. Initially, the data reveals a gradual rise in humidity, exceeding 50, which is then followed by a consistent decline back to 50. This trend could be affected by seasonal variations, weather patterns, and other environmental factors. Grasping these changes in humidity is crucial for agricultural planning, climate research, and indoor air quality management. The graph plotted in the above Fig. 16 serves as a clear visual tool for tracking humidity level changes over time, aiding in informed decision-making in these fields.
Fig. 16 [Images not available. See PDF.]
Humidity reading comparison in various dates
After wards, the temperature and humidity readings are recorded for 24 h and is represented as a comparative graphical representation in the aqi.in server.
The temperature readings have been recorded for the period of 24 h and have been represented as a comparative graphical representation as shown in the above Fig. 17.
Fig. 17 [Images not available. See PDF.]
Temperature readings for 24 h
The humidity readings have been recorded for the period of 24 h and have been represented as a comparative graphical representation as given in the above Fig. 18.
Fig. 18 [Images not available. See PDF.]
Humidity readings for 24 h
Summary of pollutant monitoring
This section gives the tabulation of the PM1, PM2.5, PM10, and Ozone readings recorded at various dates in the below Table 2.
Table 2. Tabulation of the PM1, PM2.5, PM10, and Ozone readings
PM1 | PM 2.5 | PM 10 | Ozone |
|---|---|---|---|
22 | 45 | 40 | 1779 |
25 | 39 | 33 | 1926 |
17 | 32 | 35 | 1853 |
27 | 54 | 48 | 2788 |
30 | 49 | 46 | 3558 |
Sensor-wise case study
Sensors like MQ2 and MQ4 have been designed to identify several varieties of gases that are present in the air. The MQ2 gas sensor measures the concentration of several gases in the atmosphere, including hydrogen, butane, propane, methane, alcohol, smoke, and LPG (i.e., Liquefied Petroleum Gas). Chemiresistor is another name for it. When the sensor material comes into touch with the gas, it changes resistance. Gas detection is done using this change in resistance value.
Alcohol, smoke, LPG, methane, hydrogen, and CO (i.e., Carbon Monoxide) concentrations may all be measured using the MQ4 natural gas sensor. It is widely used in companies and homes for identifying the oozing out gas. Potentiometers may be deployed to modify the gas sensor sensitiveness owing to its quicker reaction time and elevated level of sensitiveness.
Here, the “CO2 and CO” readings are measured using the MQ2 and MQ4 sensors, respectively. The CO and CO2 readings that these sensory devices detect are shown on an LCD and are tabulated in the following Table 3.
Table 3. Tabulation of the CO and CO2 readings
MQ2 | MQ4 |
|---|---|
1153 | 1878 |
1328 | 1387 |
1561 | 1352 |
3911 | 3196 |
2809 | 2119 |
2752 | 3579 |
2752 | 3579 |
These readings are also represented as a comparative graphical representation through Thingspeak server.
The CO readings taken at various dates are recorded and represented as a comparative graphical representation as given in the above Fig. 19. Here the CO readings have been denoted in the y-axis, whereas the dates have been denoted in the x-axis.
Fig. 19 [Images not available. See PDF.]
CO reading comparison in various dates
The CO2 readings taken at various dates are recorded and represented as a comparative graphical representation as given in the above Fig. 20. Here the CO2 readings have been denoted in the y-axis, whereas the dates have been denoted in the x-axis.
Fig. 20 [Images not available. See PDF.]
CO2 reading comparison in various dates
Comparison of the contemporary research
In this part, we will be making a comprehensive comparative study using performance indicators and miscellaneous parameters.
Comparison between the actual and predicted values using performance indicators
In this part, we are comparing between the actually measured sensor values and predicted values (including the atmospheric traits and air quality parameters), respectively. The same has been presented in the below Table 4.
Table 4. Comparative investigation between actual and predicted values of traits/ parameters
Parameters | Actual | Predicted |
|---|---|---|
PM1 | 18 | 25 |
PM2.5 | 22 | 22 |
PM10 | 45 | 45 |
Temp. Values (Sup.) | 40 | 40 |
MQ2 values (Sup.) | 29 | 29 |
MQ4 values (Sup.) | 1153 | 1153 |
Ozone | 1878 | 1779 |
As seen in the above tabulation, the actual and predicted values were equal for traits/ parameters like PM2.5, PM10, Temp. values (Sup.), MQ2 values (Sup.), and MQ4 values (Sup.). This means that there were no errors in the prediction of these traits/ parameters. For the rest of the traits/ parameters like PM1 and ozone, there were no considerable deviation, and the actual values and predicted values were closer to each other.
(Note: (Sup.) denotes the observations done using supplementary sensors, which were not part of the major segment of our work.)
We are validating the performance of predictions made for above traits/ parameters with the help of following four performance indicators:
Now, we are comparing the proposed methodology and 2 of the sensing elements used by the contemporary work [138] in the below Table 5.
Table 5. Comparative investigation between proposed and contemporary monitoring methodologies using performance indicators
Methodology | MAE | MBE | RMSE | R2 |
|---|---|---|---|---|
[138] (Uhoo sensing element) | 17.7 | 15.4 | 22.4 | 0 |
[138] (Hanvon N1 sensing element) | 18.1 | 17.4 | 24.1 | 0.56 |
The proposed Methodology | 15.15 | 13.14 | 37.52 | 0.86 |
As seen in the above tabulation, MAE and MBSE of our proposed methodology were better than the compared sensing elements of the contemporary work [138] (bold in the table). However, the RMSE was not up to the mark than the compared sensing elements of the contemporary work [138]. Finally, the R2 value (0.86) was generated based on our research segments.
Comparison using miscellaneous parameters
In this part, we are comparing the proposed methodology and other 2 contemporary monitoring methodologies in means of parameters like observations made, embedded hardware, and final resultant in the below Table 6. When compared, this proposed methodology studies many parameters/ traits than the other 2 contemporary monitoring methodologies [25, 26]. Furthermore, this proposed methodology makes use of 2 microcontrollers, whereas the other 2 contemporary monitoring methodologies used only one microcontroller for data processing and interpretation.
Table 6. Comparative investigation between proposed and other 2 contemporary monitoring methodologies
Methodology | Observations made | Embedded hardware | Final resultant |
|---|---|---|---|
[26] | Air quality observations related to both humidity and Temperature | Arduino Uno MQ135 DC Motor DHT11 | A scaler variable to adjust the rate of operation of a ventilator mechanical unit to lessen atmospheric contaminations |
[25] | Air quality observations related to Temperature only | Arduino Uno RF Module MQ7 DHT22 LCD | Atmospheric contaminations-based harzards with regard to site position |
The proposed Methodology | Air quality observations related to several traits/ parameters by including but not limited to temperature, pressure, humidity, PM1, PM2.5, PM10, and ozone | Arduino Uno Raspberry Pi Analog-to-digital converter Ozone sensor PMS7003 sensor LCD | Measurement of PM1, PM2.5, PM10, and ozone particles alongside the extensive measurement using sensors like temperature, MQ2, and MQ4 sensors to reveal the complicated linkages across these airborne contaminants and gain knowledge about their interaction considering the atmospheric traits like temperature, pressure, and humidity |
Conclusion and future work
We have successfully integrated two microcontrollers -Arduino Uno and Raspberry Pi along with the primary measuring sensors like ozone sensor and PM7003 sensor and carried out the characterization of air quality and variability investigation of traits/ several parameters. The inclusion of two microcontrollers made our air quality investigation method to be interactive and effective, which aids in the evaluation of spatial changes and time patterns. We investigated both the atmospheric traits and air quality-influencing parameters in this work for characterization and variability investigation, which revealed the complicated linkages across these airborne contaminants and gain knowledge about the way they interact. The obtained results were comprehensively investigated and compared with five deductions to make our findings intuitive.
Our study does not use sensors specifically designed for detecting UFPs (ultrafines) as in many works. However, we have emphasized the focus on PM1 and PM2.5, which are also significant for health impacts. So, our future research will be considering incorporation of UFP-measurable sensors to comprehensively address the full range of airborne particle sizes and their health effects.
Author contributions
M. C contributed to the study conception and design. Hardware, Software, Material Preparation, Data Collection, Analysis & wrote the paper. M.V. L contributed in Supervision, review and edited it. P.T contributed in Supervision, review and editing. All authors read and approved the final manuscript.
Data availability
All data generated or analyzed during this study are included in this published article and its supplementary information files.
Declarations
Competing interests
The authors declare no competing interests.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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