Yu-Lin Zhao1, Jiali Tang2, Han-Pang Huang1, Ze Wang3, Tse-Lun Chen4, Chih-Wei Chiang  5, Pen-Chi Chiang  4

 

1 Department of Mechanical Engineering, National Taiwan University, Taipei 10673, Taiwan
2 College of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China
3 School of Material Engineering, Jiangsu University of Technology, Changzhou 213001, China
4 Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 10673, Taiwan
5 Department of Mechanical Engineering, Kun Shan University, Tainan 710303, Taiwan


 

Received: May 23, 2020
Revised: July 24, 2020
Accepted: July 26, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.2020.05.0255  

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Cite this article:

Zhao, Y.L., Tang, J., Huang, H.P., Wang, Z., Chen, T.L., Chiang, C.W. and Chiang, P.C. (2020). Development of IoT Technologies for Air Pollution Prevention and Improvement. Aerosol Air Qual. Res. 20: 2874–2888. https://doi.org/10.4209/aaqr.2020.05.0255


HIGHLIGHTS

  • IoT technologies for ambient air quality monitoring were reviewed.
  • IoT platform for ambient air quality management was established.
  • Case studies of pollution monitoring, trace, prevention and improvement were evaluated.
  • Strategies on smart air pollution control could be achieved by A-IoT technologies.
 

ABSTRACT


In order to mitigate the challenges of air pollution, a large number of Internet of Things (IoT)-related technologies have been developed to evaluate and monitor various parameters of air quality. Hence, this paper reviews the fundamental characteristics of IoT; compares and analyzes radio frequency identification (RFID), M2M, and sensor networks; and accordingly proposes an intelligent and multifunctional monitoring platform for reducing air pollution. Our results indicate that establishing systems for comprehensive network communication, cloud-based decision making, information tracking, and online management based on these technologies will improve the ambient air quality more efficiently. Furthermore, we examine several cases verifying the availability and performance of IoT ambient air quality management platforms.


Keywords: Internet of Things (IoT); Ambient air pollution; RFID; M2M; Sensor networks; IoT platform.


INTRODUCTION



Issues of Ambient Air Pollution and Instrumental Measurement

The World Health Organization (WHO) defines ambient air pollution as the potential air pollutants emitted from industries, households and vehicles. Those air pollutants include particulate matter (PM) and gaseous air pollutants. The fine particulate matter emitted from fossil fuel combustion process of vehicles, power plants, industry, households, or biomass burning has a severe effect on human health. The estimated 25% of lung cancer deaths, 8% of chronic obstructive pulmonary disease (COPD) deaths, and 15% of stroke deaths were caused by the fine particulate matter. Those particles can settle deep in the lungs resulting the decrease of lung function, chronic bronchitis and cardiac diseases. The gaseous air pollutants could also be the precursors of secondary particulate matters formation. Therefore, in order to control and assess the ambient air pollution emission, the development of instruments measurement and monitoring system for ambient air quality is inevitable approach. In general, the instruments measure for critical regulatory air pollutants such as PM (i.e., PM10 and PM2.5), sulfur oxide (SOx), nitrogen oxide (NOx), ozone (O3), carbon monoxide (CO) and volatile organic compounds (VOCs) can be monitored and real-time analyzed (Pan et al., 2018; Chen et al., 2019; He et al., 2020). For example, the aerosols and dust at the designated area are systematically monitored by different industrial proven PM technologies such as gravimetric sampling, light scanning, and inertial weighing tapered element oscillating microbalance (TEOM). The portable and personal instruments are the available options to monitor ambient air pollution in the specific workplace. The equipment can detect the presence of toxic substances to protect the people’s safety. Consequently, the application of instrumental measurement for the existence of crucial air pollutants can prevent the health and respiratory problems. According to the U.S. EPA, the criteria of ambient air monitoring strategies include: (i) establishment of air pollution data to the general public; (ii) implementation of air quality standards; (iii) evaluation of the effective air pollution control; (iv) development of the predictive air quality models; and (v) long-term studies of air pollution on the health effects.

 
Concepts of IoT

The Internet of Things (IoT) generally refers to the goals achieved by following established network communication protocols, connecting items through various networks and using radio frequency identification (RFID) technology, various sensors, location recognition systems, and information processing mechanisms. Finally, information exchange and item identification, tracking service and real-time management are realized. These physical devices from IoT can achieve collaboration and interaction without human intervention, and provide people with intelligent, intensive, and fast-response services. The IoT plays a remarkable role in many application fields including healthcare, industry, entertainments, agriculture, environmental protection, and security. The pervasiveness of IoT eases some everyday activities and enhances the connection properties of people (Ammar et al., 2018; Sai et al., 2019).

From the technical level analysis, IoT can be divided into perception, network and application layers, as shown in Fig. 1.

  • Perception layer includes diverse sensor nodes and gateways, such as various sensors, electronic tags, read/write devices, cameras, and GPS, whose main function is to identify, collect and transmit information of objects.
  • Network layer is composed of various internet, wireless communication networks, mobile air networks (3G/4G/5G), private networks, network management systems, cloud computing platforms, etc. Its main role is to carry out real-time transmission and fast various data information obtained by the perception layer.
  • Application layer provides a convenient information interaction system for IoT and users according to the actual needs of users.

Fig. 1. Three layers of IoT.
Fig. 1. Three layers of IoT.

Fig. 2 presents the system model to gain an insight into the real meaning of IoT framework under the axial application development. The IoT framework is composed of a loop of real-time sensing, calculating, transmitting, and presenting data. For cost and safety reasons, not all IoT devices can directly connect to the outside world (e.g., the cloud). These devices must be connected to a hub or gateway to communicate. For the IoT frameworks considered in this survey, the cloud is the backbone, which offers databases for high-speed storage and reading of data, big data operations for analysis, and security encryption for preserving confidentiality and privacy. Customers use their smart phones, tablets, or laptops to interact with other IoT devices indirectly through Bluetooth, Wi-Fi, or mobile communication technology (Kim et al., 2017; Benammar et al., 2018).

Fig. 2. IoT system model. 
Fig. 2. IoT system model.

IoT Technologies for Ambient Air Quality Monitoring

IoT makes full use of next-generation IT technology in all aspects of life and integrates the existing Internet with IoT to achieve the integration of human society and physical systems, as shown in Fig. 3. In this integrated network, there are server platforms that can achieve real-time management and interact with people, machines, equipment and infrastructure components within the integrated network. Therefore, human beings can manage life cycle of production in a more refined and dynamic manner, reach a “smart” state, improve resource utilization and productivity levels, and strengthen the relationship between man and nature. Specific applications of IoT include the areas such as intelligent transportation, environmental protection, e-government, public safety, industrial monitoring, and safe home.

Fig. 3. IoT applications.
Fig. 3.
 IoT applications.

Regarding ambient air quality monitoring, developed countries such as some from Europe and the United States have carried out many related studies. The IoT-based air quality monitoring platform could perform online monitoring and real-time response, and realizes 24 hours of air condition monitoring throughout the day (Postolache et al., 2009). Pillai et al. (2010) proposed the method of using intelligent sensor networks for monitoring. It was also proved that the air quality has a great impact on human health with monitoring indoor air conditions. Marinov et al. (2016) studied the air quality monitoring system that was applied in urban environments. The system consisted of sensor nodes, wireless routers, servers and terminals. The sensor node was connected to the router and sent the collected data to the server via Wi-Fi. The terminal could access the server to obtain sensor data. Telegram et al. (2017) proposed an air quality monitoring system based on ZigBee, and used LabVIEW to draw a graphical interface on the monitor, and finally tested it on a campus to obtain good results. Kim et al. (2014) implemented an integrated indoor air quality monitoring system that could collect multiple-gas concentration information simultaneously. The system was optimized to improve fault tolerance, reduce network congestion and system power consumption based on an algorithm calculation. Firdhous et al. (2017) proposed an indoor air quality monitoring system based on IoT, which collected and transmitted data to the gateway node with 5 minute intervals via a Bluetooth connection.

The gateway node communicated with the processor via Wi-Fi. The system met the requirement of tracking the ozone concentration near the photocopier. Yang and Li (2015) designed an intelligent sensor system for air quality monitoring and made a lot of research on data sharing. The system used mobile phones as a middleware to implement data pre-processing, data display and positioning functions. The sensor unit was connected to the user’s mobile phone via Bluetooth, and then the mobile phone was connected to the server, to complete the process of data interaction and sharing. As data providers and receivers, users could upload their own data and view other users’ data as well. Based on a data transmission method, Phala et al. (2016) proposed to use the Global System for Mobile Communications (GSM) to transmit collected data to a base station, and developed a graphical user interface for users to interact with the system, which could finally measure the air pollution level in real time. Wonohardjo and Kusuma (2019) used mobile sensors and evaluated two different sampling methods, which can only detect carbon monoxide pollution. Since each of the previous studies had its own unique technologies and applications, it was required to develop an open and comprehensive air pollution monitoring platform throughout this research work.


FUNDAMENTALS OF IoT


IoT technology is a kind of network technology based on internet technology. In addition, it includes end equipment and facilities for information exchange and communication. Generally speaking, three key technologies of IoT are: RFID, M2M and sensor network.

 
RFID

RFID is a non-contact automatic identification technology. It uses radio frequency signals to automatically identify target objects and to obtain related data. The identification work does not require manual intervention and can work in various harsh environments. RFID hardware consists of tags, readers and antennas. The reader sends an electronic signal through the antenna. After receiving the signal, the tag transmits the internally stored identification information. The reader then receives and recognizes the information sent back by the tag through the antenna. Finally, the reader sends the recognition results to a host.

Fig. 4 shows the working principles of RFID. When an electronic tag enters the magnetic field recognition range of a reader, the reader encodes the information, and then transmits it to the electronic tag through an antenna through a process of simultaneously releasing radio wave energy. Passive electronic tags or semi-active electronic tags within the working recognition range of the reader receive this pulse signal and use the magnetic field energy induced from the reader to convert the magnetic field energy into electrical energy to generate an induced current. The closed circuit performs a series of processing and reasoning on the received signal. The electronic tag sends a read-only command by extracting information from its own storage unit through the control logic circuit, and then sends this information to the reader after a series of processing. If the command sent by the reader is a rewrite command, the working voltage of the control logic circuit in the electronic tag is increased, and then the relevant content in the EEPROM is modified.

Fig. 4. Working principle chart of RFID.
Fig. 4.
 Working principle chart of RFID.

The development of RFID technology can be divided into the following stages:

  • 1941–1950: RFID technology was proposed on the basis of the radar concept, which laid the theoretical foundation.

  • 1951–1960: Many RFID technology exploration projects were widely conducted in the research field of the laboratory.

  • 1961–1970: RFID technology had gradually shifted from theory to application attempts.

  • 1971–1980: RFID had been developing rapidly and some RFID applications emerged.

  • 1981–1990: RFID technology and derivative products entered into a commercial stage and various applications had appeared.

  • 1991–2000: The standardization of RFID technology began to be valued, and a series of RFID products began to be applied in people’s daily activities.

  • 2001–present: The types of RFID products become more and more abundant, and the production costs of electronic tags have gradually reduced. Active, passive, and semi-active electronic tags are commonly used in different fields, and the issue of RFID standardization is receiving increasing attention.

 
M2M

M2M is a general term for all technologies that enhance the communication and network capabilities of machines and equipment. As a natural extension of embedded control, it allows machines and equipment applications to share information with host information systems and among operators. It provides a means for the device to establish a wireless connection between systems, remote devices or/and individuals in real time to transmit data. M2M includes the domain of machine to machine, man to machine, machine to man, machine to mobile, and mobile to machine (Toma et al., 2019).

As shown in Fig. 5, M2M’s products include the wireless terminal, transmission channel and industry application center. Rather than the usual mobile phone or laptop, the wireless terminal refers to a special industrial application terminal. The transmission channel is the channel from the wireless terminal to the user’s industry application center. The industry application center is a convergence point for uploading data from terminals, and keeps an eye on scattered industry terminals. The characteristic is that the industry has strong characteristics, users manage it by themselves, and it can be located at the end of the enterprise or hosted.

Fig. 5. Structure of M2M system.
Fig. 5.
 Structure of M2M system.

The M2M application market is growing rapidly worldwide. With more sophistication of related technologies including communication equipment and open-source software and protocols, the cost of M2M products has significantly dropped. The M2M industry will be gradually mature and low-cost with more standardized technologies (Zhang, 2011). At present, it has been applied in the fields of safety monitoring, mechanical services, maintenance operations, vending machines, public transportation systems, fleet management, industrial process automation, electric machinery, and urban informatization all over the world. Unlike traditional wired or wireless networks, M2M has no need for expensive cables and communication transceiver equipment; it only requires a system that is stable, cheap, efficient, and real-time under non-manual operation.

 
Sensor Network

The sensor network is an important component of the IoT to recognize material world. It refers to a huge network formed by combining information sensors such as infrared sensors, global positioning systems, and laser scanners with the Internet. The sensor network allows all components connecting to the network for easy identification and management. It uses the sensors built in the node to measure various signals in the surrounding environment, where to detect material phenomena including temperature, humidity, noise, light intensity, pressure, soil composition, size, speed, and direction of moving objects.

The basic feature of a sensor network is dynamically self-organizational, and user terminals can move freely within the network while maintaining communication. The dynamically self-organizational networks can use the routing and forwarding capabilities of mobile terminals to communicate without an infrastructure set-up. The main types of nodes are sensor nodes, aggregation nodes and management nodes. The various nodes in the system are primarily connected by dynamically self-organizing topology. Typically, the composition of network nodes includes the following four parts: sensing mechanism, processing mechanism, communication mechanism and power supply. Other functional elements include: positioning system, motion system, and power generation device. In view of the characteristics of limited number of nodes and irreplaceable energy, technicians are trying to find a method that can control the number of nodes and can effectively use node energy to maximize the number of nodes and save energy. In addition, to extend the service life of nodes is another important research goal to effectively utilize each node. As for wireless sensor networks, an efficient network topology is the basis of network technologies. It can greatly improve the utilization efficiency of network communication protocols and extend the life cycle of the network. Under certain conditions, the network topology can be optimized by the node selection strategy for the network to avoid the emergence of redundant lines between nodes, and to maximize information transmission. Currently, there are two main methods of topology control:

  • Hierarchical topology control: A clustering idea is adopted, first to select some nodes in the network as cluster head nodes, and then to connect these cluster head nodes to communicate with each other, and finally to constitute an optimized transmission system for information processing and transmission, as shown in Fig. 6.

Fig. 6. Structure of hierarchical topology control.
Fig. 6. Structure of hierarchical topology control.

Power-type topology control: The main idea is to adjust the actual power of each node in the network to achieve a dynamic adaptive balance of routing efficiency, number of neighboring nodes, and network energy consumption while ensuring network connectivity.

 
DEVELOPMENT OF IoT PLATFORM FOR AMBIENT AIR QUALITY MANAGEMENT


This IoT system used JavaScript library to write dynamic webpage program, and integrated Arduino Ethernet and jQuery UI to build up an interactive interface for the exchange of webpage information. The NPM (Node Package Manager) management module tool and Express framework are used to develop website application. The application (“apps”) of cell phone and personal access devices (PADs) are developed by using HTML and JavaScript for the end users.

The current environmental monitoring technologies are mainly ground-based passive monitoring systems, which are still insufficient for real-time transmission and spatial distribution of pollutants, data collection, and real-time information integration. Therefore, the IoT technology is used in conjunction with relevant scientific advanced equipment to perform air quality monitoring, and provide real-time environmental information for policy planning, information advocacy, and real-time pollution inspection and prevention.

Table 1 presents comparisons of IoT technologies and applications which indicates that technologies of IoT can be classified into sampling technique, data processing, commutation method and system integration, which can be utilized in the area of indoor and ambient quality management. With the development of IoT technologies, in this research work, our IoT platform of ambient air pollution control was expected to monitor the pollution concentration in real time and identify the pollution type in a specific area by using open-path Fourier transform infrared (OP-FTIR) and miscellaneous gas sensors (MGSs). When the air pollution concentration is over the ambient standard, the Light Detection and Ranging (LiDAR) system is triggered to gather the information of pollution on different locations, both spatially and temporally, to understand the processes and trajectories of the atmospheric pollutants. The source of pollution traced by LiDAR will be monitored with miniature continuous emission monitoring systems (Mini-CEMSs) continuously. The scientific instruments use a variety of communication methods including the NarrowBand Internet of Things (NB-IoT) and Wi-Fi with terminals, i.e., mobile computers and phones, which are not utilized in conventional instrument and traditional technology. In addition, to adapt to different communication distances and device requirements, the appropriate method could be developed to make the system more robust, user-friendly, and inexpensive. The details of the environmental applications extended from the platform were described in the section “Application of IoT Platform.”

Table 1. Comparisons of IoT technologies and applications.

 
IoT Platform of Ambient Air Pollution

This environmental air pollution IoT monitoring system (shown in Fig. 1) is a network of multiple scientific instruments (LiDAR, OP-FTIR, mini-CEMSs, MGSs, anemometers) and a web connected by the NarrowBand Internet of Things wireless communication technology. The NB-IoT is a standards-based low-power wide area (LPWA) technology (Alliance, 2015), a unified standard for global telecommunications characterized by wide coverage with low power consumption and cost, and suited for IoT devices in radio interference environments. This monitoring platform (Fig. 7) is composed of NB-IoT networked scientific instruments (LiDAR, OP-FTIR, mini-CEMSs, MGSs, anemometers) and web application software to complete environmental air pollution monitoring network and to realize real-time monitoring around the clock. The monitoring system is composed of three layers of sensors, data transmission and application software. By utilizing the characteristics of NB-IoT’s wide coverage, low power consumption and large connection, the system realizes the uploading of monitoring data and the sending of instructions. The system software mainly includes two parts: embedded software and server-side web application software. The embedded software is responsible for uniformly coordinating and integrating the hardware modules of the system to ensure the stable and orderly operation of the monitoring system. As for system server side, MySQL is used to complete the monitoring database design. The web application software implements the management of user information and the storage and query of monitoring data. At the same time, the geographic information system (GIS) is used to implementthe visual display of scientific instrument control and the monitoring data of environmental air pollution. The functions of automate, intelligent, network and systemize, data monitoring and tracking can be integrated into the communication technologies. The industrial operators can self-control through public information to achieve a robust air quality management.

Fig. 7. Air pollution monitoring platform of Internet of Things.
Fig. 7.
 Air pollution monitoring platform of Internet of Things.

The pollutants’ real-time data and control target (industrial zone factory)’s related information can be uploaded to the cloud database. The platform employs Eclipse software to develop mobile audit apps and provide relevant personnel the query and transport of the data. In addition, the platform applies two-way information protocol to compare and to analyze for the air pollution monitoring, location distribution, manufacturer information, configuration of equipment and waste storage facilities, and operating permits. Finally, the platform provides analysis of the potential pollution areas of environmental protection units, identifies the source of discharged pollution, and then assists on-site inspection and improves the efficiency of supervision and management.

 
Environmental Air Monitoring Sensors

The gas sensor is a device that converts a specific gas concentration into an appropriate electronic signal, such as voltage, current, or resistance. Jones et al. (1987), in the Health and Safety Executive Laboratories of UK used a catalytic sensor to monitor the explosive mixture of methane and air. This device is prepared on the surface of platinum wire for high-temperature resistance. The catalyst layer and the heater are used to keep the catalyst at a high temperature to ensure the rapid combustion of flammable gas molecules. When the air and the combustible gas are mixed, the combustible gas is oxidized and contacts the catalyst to generate heat, which causes the surface temperature of the platinum wire to rise. The higher the temperature is, the greater the resistance is. As such, the concentration of specific gas can be identified by obtaining an electronic signal and measuring the change in the resistance value of the platinum wire.

In 2001, Vincenzi et al. (2001) employed the screen printing method by attaching palladium with tin oxide to distinguish gases such as carbon monoxide, methane and nitrogen dioxide. Nguyen et al. (2011) presented a 1-D tungsten oxide nanowire produced by electrospinning, which has a measurable minimum concentration of ammonia around 10 ppm. Mane et al. (2014) published the tungsten oxide thin film by sol-gel method for nitrogen dioxide measurement. It was found that the film has a fast response and the lowest concentration that can be measured is 5 ppm. Chinh et al. (2014) used thermal evaporation to produce a 1-D structure, and then applied yellow photolithography technology to produce electrodes and self-heating devices. It is possible to measure 10 ppm of nitrogen dioxide gas (Molenar, 2005). The typical gas sensors such as VOCs, CO, SOx, NOx are based on metal oxide semiconductors (MOSs). The sensor component is a micro-electromechanical system (MEM) device using silicon wafer technology to implement in an approximately 1-µm-thin silicon nitride membrane to achieve ultimate stability and lowest possible power consumption. The catalytically active material shows a gas-concentration-dependent resistance change.

Since the value of the gas sensor has drift due to long-term continuous working time or aging, the gas sensor needs to be calibrated by connecting the standard gas in the gas cylinder, adjusting the specified flow rate, and resistance value. In addition, some new sensors can possess self-correct function based on the relationships among the temperature, gas sensor values, the voltage, and current in the circuit.

As for the concentration of suspended particles, it is measured by using light-scattering theory. When the particles pass through the laser light, scattering and light attenuation would be generated. The measurement and analysis of different particle sizes are carried out by the Mie optical scattering theory algorithm.

 
Networked Device

The internal network of IoT platform includes LiDAR, OP-FTIR, mini-CEMSs, MGSs, and anemometers, as briefly described below.

 
LiDAR System

As shown in Fig. 8(a), the Light Detection and Ranging system is a self-made air pollution optical telemetry instrument. It consists of a laser transmitter and a telescope as a receiver. The specific backscatter signals are measured by photomultiplier tube installed behind the telescope. The system is mounted on a vibration-isolated scanner with good structural stability for the pollution measurement at the vertical elevation. It has a wide-area and real-time feature for air pollution monitoring, which can break through the existing bottlenecks of passive monitoring instruments and measure hot zones, spatial concentrations, and diffusion ranges of air pollutant.

Fig. 8. Networked device: (a) LiDAR, (b) OP-FTIR, (c) mini-CEMS, and (d) MGS.
Fig. 8. Networked device: (a) LiDAR, (b) OP-FTIR, (c) mini-CEMS, and (d) MGS.

 
OP-FTIR

Open-path Fourier transform infrared is shown in Fig. 8(b). The volatile pollutants in the air are monitored, and the average concentration of the gas sample in the measurement path is obtained by spectral analysis.

 
Mini-CEMS

In order to reduce the monitoring misinterpretation of high-concentration pollution in costly pipelines, the miniature continuous emission monitoring system (as shown in Fig. 8(c)) uses an automatic start-up device to dilute the standard air to a measurable range according to the concentration of VOCs extracted from the emission outlet of the blowdown piping by the aspirator of mini-CEMS, and automatically derives it. The concentration of native VOCs is obtained, and the measurement data is automatically and continuously uploaded to the cloud server. This system can be installed in the pipeline before/after prevention and control equipment to measure the concentration of pollution before/after the equipment. It can also be used to evaluate the effectiveness of the equipment and reduce the high-altitude operation of the chimney. Inspection can be performed directly with standard gases, ensure the accuracy of the instrument and upload measurement data in real time over the network to provide environmental-protection-related personnel’s query reference.

 
Miscellaneous Gas Sensor (MGS)

Hybrid gas sensors use metal oxide semiconductors to detect polluted gases and optical scattering to detect suspended particles (PM). These low-cost sensors have been widely used in real-time air quality monitoring. The self-developed composite gas sensor (Fig. 8(d)) integrates multiple sensors for measurement of VOCs, O3, CO, SO2, PM10, PM2.5, temperature, humidity, etc. The specifications regarding concentration range, heater voltage, power and accuracy of sensors are shown in Table 2. The installed conditions of MGSs are similar to that of the air quality monitoring equipment set by the Environmental Protection Agency. The MGS can upload a large amount of relevant continuous monitoring data to the cloud system in a real-time manner.

Table 2. The specifications of sensors.

 
APPLICATION OF IOT PLATFORM


 
Pollution Monitoring and Transport


The Spatial Concentration of Dust Emissions

The tracking source and monitoring dispersion of dust are rather difficult, an IoT system makes an attempt for the transportation and flux estimation of particle concentration by using LiDAR and MGS to investigate the dynamical process of particle and their relationship with air quality. The results (Fig. 9) show that the material storage eroded by wind with speed about 3.6 m s−1 could cause particle emission to the maximum height of 20 m above the material storage, and produce the flux of dust around 674 mg s−1. The flux of dust is also proportional to the dust mass concentration (PM10) measured by ambient particular monitors.

Fig. 9. Spatial variation of dust above the material storage. (Note: Inserted box shows the area of spatial variation of dust for the flux estimation of dust concentration.)Fig. 9. Spatial variation of dust above the material storage. (Note: Inserted box shows the area of spatial variation of dust for the flux estimation of dust concentration.)

The transportation and diffusion of particles can be proved by using controllable particle generator under specific weather condition. The artificial particle measured by MGS settles every 5 m on the transmission path. The experimental results show that the average wind speed is between 1 m s−1 and 3 m s−1 when the prevailing wind is southwest (200–300°). The artificial particles can be flown to at least 60 m. 

It is indicated that the distance where is the most floated dust settled. However, the fine particles diluted in atmosphere can be most likely dispersed more to 130 m in distance and 10 m in height from the outlet of particle generator measured by LiDAR before settlement. The results are further estimated by vertical wind speed 0.2–0.5 m s−1 measured by the anemometer. The vertical wind speed is used to determine the time about 20–50 s when the particles rise 10 m. When the horizontal wind speed is 3 m s−1 at this time, the particle can be dispersed about 60–150 m in theory, which is consistent with the experimental results.

 
The Sediment Pollution from Refinery

The IoT technique will be useful to enhance the effectiveness of inspection and to strengthen the capability of the environmental monitoring by integrating information immediately. It is accomplished to use the 3-D scanning LiDAR system to track the path of air pollution emissions first, then monitor the range of pollution diffusion, and finally cooperate with the analysis of pollution components to confirm whether the polluted area is responsible for specific pollution emissions. The method and observation results are described as follows: The spatial distribution of pollution is monitored by LiDAR scanning monitoring operation. The observation shows that the pollution diffusion range is shown in Fig. 10, and the FTIR is set up at the pollution settlement denotes as ▲ shown in Fig. 11 to analyze the pollution components which indicates that the situation of pollution-free settlement mainly occurs at 1–3 a.m.

Fig. 10. Optical radar vertical scan pollution distribution diagram.
Fig. 10.
 Optical radar vertical scan pollution distribution diagram.

Fig. 11. Vertical scanning result of the second measuring line in Fig. 9. (Note: ▲ is the location of FTIR.)Fig. 11. Vertical scanning result of the second measuring line in Fig. 9. (Note: ▲ is the location of FTIR.)

The prevailing wind direction is southerly. When no settlement occurs, the concentration values of all pollutants measured by OP-FTIR are lower than the ones of their individual pollution. The average value of the objects indicates that the concentration of pollutants measured on the ground is low when no atmospheric settlement occurs. The vertical distribution of pollution measured by LiDAR found that the settlement time more likely occurs from 8 p.m. to about 1 a.m. the next day. The main prevailing wind direction is northwest wind to northeast wind. When the pollution subsides and the northwest wind prevails, the N,N-dimethylformamide measured by OP-FTIR has a higher concentration (17–60 ppb). Furthermore, the vertical distribution of pollution measured by LiDAR has found that sometimes non-sedimentation may occur, which may originate from low-level transmission. The time, path and composition of the pollution can be used to trace the source of the pollution. It is worth mentioning that the pollution of the refinery in this case can affect the ambient air quality up to 3 km away.

 
Pollution Trace and Prevention

Another application example of this technology confirms the settlement position of the dust in the Zhuoshui River to facilitate the planting of vegetation and to abate wind erosion of dust. Zhuoshui River, the longest river running through central Taiwan, is about 186.6 km long and covers a basin area of 3,156.90 km2. As the river name suggested, its stream is entrained with a large amount of sediment and is turbid for many years. The annual precipitation of the mountain catchment area in the upstream reaches 2–3 m, and the strata of passing through are mostly slate, shale, and sandstone that are susceptible to erosion. Therefore, the sand content is high and the local residents have the saying “meal with sand disturbance.” To this end, the technology can be used to monitor the dust in the vast field, so that the vegetation can understand the diffusion range and pollution concentration of the dust in the turbid water stream, and evaluate the contribution of the dust in the turbid water stream to the air quality.

By comparing the scanning LiDAR result about 15 m directly above the ground station with the PM10 value, the correlation coefficient r2 = 0.55. It shows that the PM10 concentration in the air has a similar trend to that of local stations. When sand is rising, the monitoring results of LiDAR scanning are shown in Fig. 12(a). The suspended particles appearing in the red circle in the figure cause the concentration of suspended particles in the air and ground stations to be 36 ± 12 µg m3 and 100 ± 20 µg m3, respectively. If it is affected by sandstorms outside the country, a layered sand and dust distribution occurs between 300–600 meters above the ground (as shown in Fig. 12(b)), and the ground station also has a large range of suspended particulates concentration increase response, sometimes the wind speed is low (0.7 ± 0.3 m s−1) The surface air quality is poor. At this time, the boundary layer height is ~250 m, the diffusion is not easy, and a similar situation occurs. This monitoring result can not only distinguish the location and the impact range of the sand, but also decide whether it is affected by the outside or inside when the ground air is bad. Consequently, the relevant units can make a correct judgment and grasp the time limit to protect and protect people’s health.

Fig. 12. (a) Light-scanning results of dust emission from turbid water stream dust. (b) Distribution of sand and dust under the influence of overseas sand and dust with height.Fig. 12. (a) Light-scanning results of dust emission from turbid water stream dust. (b) Distribution of sand and dust under the influence of overseas sand and dust with height.

 
Pollution Inspection and Improvement


The Real-time Air Quality Improvement

A research was designed to apply IoT to collect relevant information to understand the air quality situation of the athlete village in Linkou area before the World Universiade 2017, and then analyze the possible sources and causes of pollution to prevent pollution and maintain air quality. Historical data show that the main prevailing wind field during the World Universiade (19–30 August 2017) is northeast wind (from the Linkou Industrial Zone to the player village). The scanning information of the LiDAR system was used to compare and analyze the combined gas sensor measurement results and atmospheric wind field information provided between the Linkou Industrial Zone and the player village.

The results show that the main pollutants transmitted by the prevailing northeast wind in Linkou Industrial Zone were PM10, followed by pollutants such as VOCs and SO2, while pollutants such as CO and O3 did not significantly affect the player village. The system flow chart is shown as Fig. 13 to set up scientific instruments and sensors on relevant pollution transmission paths, and to study and formulate the implementation plan of relevant inspection and control measures. Compared to before/after the World Universiade as shown in Fig. 14, the air quality of the player village during the World Universiade significantly improved when environmental protection personnel carried out inspection and control using online information of the air pollution.

Fig. 13. Implementation flowchart of pollution inspection control.
Fig. 13. Implementation flowchart of pollution inspection control.

Fig. 14. Distribution of PM10, SO2, CO, and O3 concentrations over time at the Linkou Environmental Station during the World Universiade.Fig. 14. Distribution of PM10, SO2, CO, and O3 concentrations over time at the Linkou Environmental Station during the World Universiade.

 
The Pollution Prevention in Industrial Areas

Using networked scientific instruments; cloud information management systems and databases; long-term recording of monitoring data, air quality, and meteorological parameters; etc., statistically summarize and master air quality, atmospheric conditions, spatial distribution of pollution, and sources of emissions. Based on the relevant monitoring data, our research conducted a pollution inspection and prevention action plan (as shown in Fig. 13), installed an automatic continuous monitoring system (Fig. 14) for pollution sources, and monitored pollution emissions and plant prevention and control operations for a long time. The research also assisted relevant personnel to conduct real-time air pollution inspection and prevention measures. The staff of the Environmental Protection Agency used the statistical information provided by IoT to understand and strengthen the audit of abnormal air emissions which exceed the mean measured concentration with one standard deviation. The results provided real-time public information to polluters that exceeded the standards and repeatedly persuaded them and to take the initiative to explain and respond to the situation during the time of exceeding the standards. Compared with the same period in 2018 (without related equipment setting and control), the current pollution impact situation is improved by about 7–16%, as shown in Fig. 15.

Fig. 15. Monthly changes in suspended particulate concentration from 2018 (before implementation of related equipment and measures) to 2019 (after implementation of related equipment and measures).Fig. 15. Monthly changes in suspended particulate concentration from 2018 (before implementation of related equipment and measures) to 2019 (after implementation of related equipment and measures).

It was thus concluded that a large-scale IoT system was constructed with multiple applications such as pollution monitoring and transport, pollution trace and prevention, pollution inspection and improvement throughout this research work. With the development of IoT technologies, the results of this research can not only provide the law enforcement and policy formulation for the government and the public, but also improve the automation system of production for the manufacturers. Many advanced technologies and instruments are integrated into this system, including NB-IoT, LiDAR, OP-FTIR, mini-CEMSs, MGSs, and anemometers, for ambient air quality management.

 
CHALLENGES AND PROSPECTS


In developing IoT technologies for smart ambient air quality management, the major challenge revolves around embedding intelligence into common electrical devices, thus enabling them to perform autonomously and reliably. A secondary challenge involves coordinating the heterogeneous platforms of multiple devices and their unique features so that they function as a unified network for a comprehensive goal.

Thus, this paper proposes a stable and efficient system for obtaining air monitoring data from various IoT devices and performing massive cloud-based calculations. The readability and maintainability of the programming enable highly extensible connectivity via software and hardware, and the system simultaneously assesses and controls environmental air quality by analyzing big data.

In greater detail, an intelligent air quality monitoring system that embodies flexibility, reliability, effectiveness, and economics (FREE) can be developed by integrating microcontrollers, wireless sensor networks (WSNs), cloud service, the Global Positioning System (GPS), and the Android platform. The Air Benefit and Cost and Attainment Assessment System (ABaCAS; www.abacas-dss.com), which provides decision support tools, including the International Cost Estimate Tool (ICET), Response Surface Model (RSM), Software of Model Attainment Test (SMAT), and Environmental Benefits Mapping and Analysis Program (BenMAP), should be employed during project planning to configure different control scenarios and identify cost-effective strategies.

 
ACKNOWLEDGMENTS


High appreciation goes to the Ministry of Science and Technology (MOST) of Taiwan (Grant Nos. MOST 107-2221-E-002-009-MY3 and MOST 108-2911-I-002-549) for the financial support.


REFERENCES


  1. Alirezaie, M. and Loutfi, A. (2015). Reasoning for sensor data interpretation: An application to air quality monitoring. J. Ambient Intell. Smart Environ. 7: 579–597 https://doi.org/10.3233/AIS-150323

  2. Alliance, L. (2015). LPWA Technologies Unlock New IoT Market Potential, Machina Research, Nov, 2015. https://www.lora-alliance.org/portals/0/documents/whitepapers/LoRa-Alliance-Whitepaper-LPWA-Technologies.pdf

  3. Ammar, M., Russello, G. and Crispo, B. (2018). Internet of things: A survey on the security of iot frameworks. J. Inf. Secur. Appl. 38: 8–27. https://doi.org/10.1016/j.jisa.2017.11.002

  4. Benammar, M., Abdaoui, A., Ahmad, S.H.M., Touati, F. and Kadri, A. (2018). A modular iot platform for real-time indoor air quality monitoring. Sensors 18: 581. https://doi.org/10.3390/s18020581

  5. Chen, S., Cui, K., Yu, T.Y., Chao, H.R., Hsu, Y.C., Lu, I.C., Arcega, R.D., Tsai, M.H., Lin, S.L., Chao, W.C.J.A. and Research, A.Q. (2019). A big data analysis of PM2.5 and PM10 from low cost air quality sensors near traffic areas. Aerosol Air Qual. Res. 19: 1721–1733. https://doi.org/10.4209/aaqr.2019.06.0328

  6. Chinh, N.D., Van Toan, N., Van Quang, V., Van Duy, N., Hoa, N.D., Van Hieu, N.J.S. and Chemical, A.B. (2014). Comparative NO2 gas-sensing performance of the self-heated individual, multiple and networked SnO2 nanowire sensors fabricated by a simple process. Sens. Actuators, B 201: 7–12. https://doi.org/10.1016/j.snb.2014.04.095

  7. Firdhous, M., Sudantha, B. and Karunaratne, P. (2017). IoT enabled proactive indoor air quality monitoring system for sustainable health management. 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), pp. 216–221.

  8. He, X., Lin, M., Chen, T.L., Lue, B., Tseng, P.C., Cao, W., Chiang, P.C.J.A. and Research, A.Q. (2020). Implementation plan for low-carbon resilient city towards sustainable development goals: Challenges and perspectives. Aerosol Air Qual. Res. 20: 444–464. https://doi.org/10.4209/aaqr.2019.11.0568

  9. Jones, W.D., Foster, G.P. and Putinas, J.M. (1987). The catalytic activation and functionalization of carbon-hydrogen bonds. Aldimine formation by the insertion of isonitriles into aromatic carbon-hydrogen bonds. J. Am. Chem. Soc. 109: 5047–5048. https://doi.org/10.1021/ja00250a060

  10. Kim, J.Y., Chu, C.H. and Shin, S.-M.J.I.S.J. (2014). ISSAQ: An integrated sensing systems for real-time indoor air quality monitoring. IEEE Sensors J. 14: 4230–4244. https://doi.org/10.1109/JSEN.2014.2359832

  11. Kim, J. and Hwangbo, H. (2018). Sensor-based optimization model for air quality improvement in Home IoT. Sensors 18: 959. https://doi.org/10.3390/s18040959

  12. Kim, S.H., Jeong, J.M., Hwang, M.T. and Kang, C.S. (2017). Development of an IoT-based atmospheric environment monitoring system. 2017 International Conference on Information and Communication Technology Convergence (ICTC), pp. 861–863.

  13. Mane, A.T., Kulkarni, S.B., Navale, S.T., Ghanwat, A.A., Shinde, N.M., Kim, J., Patil, V.B. (2014). NO2 sensing properties of nanostructured tungsten oxide thin films. Ceram. Int. 40: 16495–16502. https://doi.org/10.1016/j.ceramint.2014.08.001

  14. Marinov, M.B., Topalov, I., Gieva, E. and Nikolov, G. (2016) Air quality monitoring in urban environments. 39th International Spring Seminar on Electronics Technology (ISSE) Pilsen, pp. 443–448

  15. Molenar, J.V. (2005). Theoretical analysis of PM2.5 mass measurements by nephelometry-# 110, Colorado State University Press.

  16. Nguyen, T.A., Park, S., Kim, J.B., Kim, T.K., Seong, G.H., Choo, J. and Kim, Y.S. (2011). Polycrystalline tungsten oxide nanofibers for gas-sensing applications. Sens. Actuators, B 160: 549–554. https://doi.org/10.1016/j.snb.2011.08.028

  17. Pan, S.Y., Ling, T.C., Park, A.H.A. and Chiang, P.C. (2018). An overview: Reaction mechanisms and modelling of CO2 utilization via mineralization. Aerosol Air Qual. Res. 18: 829–848. https://doi.org/10.4209/aaqr.2018.03.0093

  18. Phala, K.S.E., Kumar, A. and Hancke, G.P. (2016). Air quality monitoring system based on ISO/IEC/IEEE 21451 standards. IEEE Sensors J. 16: 5037–5045. https://doi.org/10.1109/JSEN.2016.2555935

  19. Pillai, M.A., Veerasingam, S. and Sai, D.Y. (2010). CAN based smart sensor network for indoor air quality monitoring. Proc. 3rd International Conference on Computer Science and Information Technology, 2010, IEEE, pp. 456–460.

  20. Postolache, O.A., Pereira, J.M.D., Girao, P.M.B.S. (2009). Smart sensors network for air quality monitoring applications. IEEE Trans. Instrum. Meas. 58: 3253–3262. https://doi.org/10.1109/TIM.2009.2022372

  21. Sai, K.B.K., Subbareddy, S.R., Luhach, A.K.J.S.C.P. and Experience (2019). IOT based Air Quality Monitoring System Using MQ135 and MQ7 with Machine Learning Analysis. Scalable Comput. Pract. Experience 20: 599–606. https://doi.org/10.12694/scpe.v20i4.1561

  22. Telagam, N., Kandasamy, N., Prasad, N.G. and Nanjundan, M. (2017). Smart sensor network based high quality air pollution monitoring system using labview. Int. J. Online Eng. 13: 79–87.

  23. Toma, C., Alexandru, A., Popa, M. and Zamfiroiu, A. (2019). IoT solution for smart cities' pollution monitoring and the security challenges. Sensors 19: 3401. https://doi.org/10.3390/s19153401

  24. Vincenzi, D., Butturi, M.A., Guidi, V., Carotta, M.C., Martinelli, G., Guarnieri, V., Brida, S., Margesin, B., Giacomozzi, F., Zen, M.J.S. and Chemical, A.B. (2001). Development of a low-power thick-film gas sensor deposited by screen-printing technique onto a micromachined hotplate. Sens. Actuators, B 77: 95–99. https://doi.org/10.1016/S0925-4005(01)00679-7

  25. Wonohardjo, E.P., Kusuma, G.P. (2019). Air pollution mapping using mobile sensor based on internet of things. Procedia Computer Science 157: 638–645. https://doi.org/10.1016/j.procs.2019.08.224

  26. Yang, Y. and Li, L. (2015). A smart sensor system for air quality monitoring and massive data collection. International Conference on Information and Communication Technology Convergence (ICTC), 2015, IEEE, pp. 147–152.

  27. Zhang, L. (2011). An IoT system for environmental monitoring and protecting with heterogeneous communication networks. 6th International ICST Conference on Communications and Networking in China (CHINACOM), 2011, IEEE, pp. 1026–1031.

Aerosol Air Qual. Res. 20 :2874 -2888 . https://doi.org/10.4209/aaqr.2020.05.0255  


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