Outdoor Air Quality Monitoring Research Articles

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Air Quality Assessment Based on a Smart Locally CO2 Monitoring System with Validation by a Reference Instrument

This article deals with the development and implementation of a low-cost smart air quality monitoring device. The study aims to develop a simple, efficient, and cheap device for real-time monitoring of Carbon dioxide (CO2) concentration and atmospheric parameters such as temperature (T) and relative humidity (RH). The realized device consists of sensors, a data storage system, a programmable card, and wireless modules for Internet of Things (IoT). It is designed for indoor and outdoor air quality monitoring. Wireless communication between the device and the control PC is ensured by the ZigBee protocol. The MH-Z14A sensor was calibrated according to the manufacturer’s recommendations and then experimentally in the laboratory. A calculation of the CO2 regression line for the devices using the least squares method gave R2 = 0.6889. The slope test using the Student method at a significant coefficient of 1% threshold and 19 degrees of freedom gave t(1%; 19) = 2.539. This illustrates that the result obtained by the realized device has less than a 1% chance of being obtained at random. An investigation was carried out to assess air quality because following the proliferation of pollutants, the normal concentration of atmospheric CO2 (400 ppm) has considerably changed. Thus, the data processed are outdoor concentrations collected in November 2022 in some localities of the Adamawa Region, Cameroon. The average values of CO2 concentration measured in four subdivisions are 497.30 ± 11.32 ppm in Ngaoundéré 1, 481.83 ± 14.90 ppm in Ngaoundéré 2, 568.63 ± 25.03 ppm in Martap and, 624.14 ± 3.96 ppm in Minim. Martap and Minim are among the most important bauxite deposits in the world. These CO2 values higher than the normal atmospheric concentration indicate a moderate Air Quality Index (AQI) in these cities during the measurement period. The designed device is inexpensive, reliable, and appropriate for air quality monitoring.

Chemical sensors for outdoor air quality monitoring

Air pollution is a global threat leading to large impacts on human health and ecosystems. Emissions and concentrations have increased in many areas and worldwide. In some areas of Europe, air quality remains very poor, despite reduced emissions and concentrations in the environment. Currently the most important environmental risk to human health is air pollution, and Europeans are considered the second biggest concern for the environment, right after climate change. Major problems related to air quality have an impact on human health, and in particular on respiratory diseases. In response to the problems of poor air quality, there is political, media and public interest in air quality issues. The growing public influence over air pollution challenges, including ongoing civic scientific initiatives involved in supporting air quality monitoring and targeted initiatives to raise public awareness and change behavior, has led to increasing support and demand for measures to improve air quality. Due to increasing air pollution, great efforts are being made to develop various chemical methods and chemical sensors used to measure air quality. Cheaper gas-based sensors based on the Arduino system are available today. Given their simplicity and given that they are more cost-effective, these sensors have proven to be very useful in some situations where it is necessary to detect certain gases in the air in a very short time. Several chemical methods have been developed that can also detect certain gases in the air. In this paper, only some methods by which gases and particles can be detected will be mentioned.

An IoT System for Air Pollution Monitoring with Safe Data Transmission.

Air pollution has become a global issue due to rapid urbanization and industrialization. Bad air quality is Europe's most significant environmental health risk, causing serious health problems. External air pollution is not the only issue; internal air pollution is just as severe and can also lead to adverse health outcomes. IoT is a practical approach for monitoring and publishing real-time air quality information. Numerous IoT-based air quality monitoring systems have been proposed using micro-sensors for data collection. These systems are designed for outdoor air quality monitoring. They use sensors to measure air quality parameters such as CO2, CO, PM10, NO2, temperature, and humidity. The data are acquired with a set of sensors placed on an electric car. They are then sent to the server. Users can subscribe to the list and receive information about local pollution. This system allows real-time localized air quality monitoring and sending data to customers. The work additionally presents a secure data transmission protocol ensuring system security. This protocol provides system-wide attack resiliency and interception, which is what existing solutions do not offer.

Adjusting surface coverage of Pt nanocatalyst decoration for selectivity control in CMOS-integrated SnO2 thin film gas sensors.

Smart gas-sensor devices are of crucial importance for emerging consumer electronics and Internet-of-Things (IoT) applications, in particular for indoor and outdoor air quality monitoring (e.g., CO2 levels) or for detecting pollutants harmful for human health. Chemoresistive nanosensors based on metal-oxide semiconductors are among the most promising technologies due to their high sensitivity and suitability for scalable low-cost fabrication of miniaturised devices. However, poor selectivity between different target analytes restrains this technology from broader applicability. This is commonly addressed by chemical functionalisation of the sensor surface via catalytic nanoparticles. Yet, while the latter led to significant advances in gas selectivity, nanocatalyst decoration with precise size and coverage control remains challenging. Here, we present CMOS-integrated gas sensors based on tin oxide (SnO2) films deposited by spray pyrolysis technology, which were functionalised with platinum (Pt) nanocatalysts. We deposited size-selected Pt nanoparticles (narrow size distribution around 3 nm) by magnetron-sputtering inert-gas condensation, a technique which enables straightforward surface coverage control. The resulting impact on SnO2 sensor properties for CO and volatile organic compound (VOC) detection via functionalisation was investigated. We identified an upper threshold for nanoparticle deposition time above which increased surface coverage did not result in further CO or VOC sensitivity enhancement. Most importantly, we demonstrate a method to adjust the selectivity between these target gases by simply adjusting the Pt nanoparticle deposition time. Using a simple computational model for nanocatalyst coverage resulting from random gas-phase deposition, we support our findings and discuss the effects of nanoparticle coalescence as well as inter-particle distances on sensor functionalisation.

Design and Development of a University Outdoor Air Quality Monitoring System

An increase in air quality monitoring research using low-cost air quality sensors has been observed. Existing air quality monitoring systems provide real-time data on at most five primary pollutants, cover a short area, and could be made expandable. An air quality monitoring system utilizing a scalable, low power wide area network, centralized system, and low cost sensors is proposed. The nodes gather data on air pollutants and environmental factors. Data conversion, storage, and calculation of air quality indices were processed on the central subsystem and visualization through IoT cloud. The results suggest improvement in the design and integration of prediction models, battery, and power management techniques.

IoT sensor nodes for air pollution monitoring: A review

Air quality is an important environmental concern, as it is strictly related to human health risks and adverse eff ects on it. Monitoring air pollutants and diff erent ancillary parameters is a feasible and crucial approach to address this challenge. This task typically involves high expenses in case measurements are carried out by using conventi onal measurement instruments and human operators. However, uti lizing measurement systems with low-cost sensors can reduce the overall implementati on eff ort. The aim of this paper is to describe the sensor node architecture applicable to a general monitoring system and based on this structure, review diff erent current low-cost measurement system proposals for outdoor air quality monitoring.

Surveillance-image-based outdoor air quality monitoring

Air pollution threatens human health, necessitating effective and convenient air quality monitoring. Recently, there has been a growing interest in using camera images for air quality estimation. However, a major challenge has been nighttime detection due to the limited visibility of nighttime images. Here we present a hybrid deep learning model, capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images. Our model, which integrates a convolutional neural network (CNN) and long short-term memory (LSTM), adeptly captures spatial-temporal image features, enabling air quality estimation at any time of day, including PM2.5 and PM10 concentrations, as well as the air quality index (AQI). Compared to independent CNN networks that solely extract spatial features, our model demonstrates superior accuracy on self-constructed datasets with R2 = 0.94 and RMSE = 5.11 μg m−3 for PM2.5, R2 = 0.92 and RMSE = 7.30 μg m−3 for PM10, and R2 = 0.94 and RMSE = 5.38 for AQI. Furthermore, our model excels in daytime air quality estimation and enhances nighttime predictions, elevating overall accuracy. Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model, reaffirming its applicability and superiority for air quality monitoring.

Reliability Testing of a Low-Cost, Multi-Purpose Arduino-Based Data Logger Deployed in Several Applications Such as Outdoor Air Quality, Human Activity, Motion, and Exhaust Gas Monitoring.

This contribution shows the possibilities of applying a low-cost, multi-purpose data logger built around an Arduino Mega 2560 single-board computer. Most projects use this kind of hardware to develop single-purpose data loggers. In this work, a data logger with a more general hardware and software architecture was built to perform measurement campaigns in very different domains. The wide applicability of this data logger was demonstrated with short-term monitoring campaigns in relation to outdoor air quality, human activity in an office, motion of a journey on a bike, and exhaust gas monitoring of a diesel generator. In addition, an assessment process and corresponding evaluation framework are proposed to assess the credibility of low-cost scientific devices built in-house. The experiences acquired during the development of the system and the short measurement campaigns were used as inputs in the assessment process. The assessment showed that the system scores positively on most product-related targets. However, unexpected events affect the assessment over the longer term. This makes the development of low-cost scientific devices harder than expected. To assure stability and long-term performance of this type of design, continuous evaluation and regular engineering corrections are needed throughout longer testing periods.

2 Low-Cost Pm Sensor Performance Testing

Abstract Low-cost particulate matters sensors (LCPMS) are widely used in indoor and outdoor air quality monitoring. Previous studies have explored the accuracy and precision of sensors, showing that LCPMS were most accurate for PM with diameters below 1 μm, and they poorly measured PM in the 2.5–5 μm range. The study aimed to investigate the aspiration and transmission efficiency of LCPMS, to set up a standard aerosol mass concentration generation system, to further comprehend the performance characteristics of LCPMS, and to examine the effect of aerosol loading. Three models of Plantower sensors were tested in this work. An ultrasonic atomizing nozzle was used to generate micrometer-sized NaCl aerosol particles. A TSI aerodynamic particle sizer was used to measure the aerosol concentrations and size distributions upstream and downstream of the LCPMS, to determine the aspiration and transmission efficiency. The mass concentration, from 20 µg/m3 to 200 mg/m3, could be varied by controlling the solution feeding rate and the solution concentration. The high mass concentration was mainly designed for aerosol loading study. The results showed that the aspiration efficiencies of the tested LCPMS were all almost 100% for particle smaller than 5 µm. The transmission efficiency of these sensors was function of particle size and strongly dependent on sampling flow. All sensors tested showed significant performance degrading when challenged with high mass concentration. The orientation of the sensor also played a role affecting the aerosol loading. These sensors performed better when mass concentration is below 100 µg/m3.

Electronic nose based on multiple electrospinning nanofibers sensor array and application in gas classification

To mimic the human olfactory system, an electronic nose (E-nose, also known as artificial olfactory) has been proposed based on a multiple gas sensor array and a pattern recognition algorithm. Detection of volatile organic components (VOCs) has many potential applications in breath analysis, food quality estimation, and indoor and outdoor air quality monitoring, etc. In this study, a facile single-needle electrospinning technology was applied to develop the four different semiconductor metal oxide (MOS) nanofibers sensor arrays (SnO2, CuO, In2O3 and ZnO, respectively). The array shows a smooth surface and constant diameter of nanofiber (average of 150 nm) resulting in high sensitivity to multiple target analyte gases. Five human health related VOCs gases were measured by fabricated E-nose and different response patterns were obtained from four MOS nanofibers sensors. Combined with feature extraction from the response curves, a principal component analysis (PCA) algorithm was applied to reduce the dimension of feature matrix, Thus, the fabricated E-nose system successfully discriminated five different VOCs gases. Real-time and non-invasive gas monitoring by E-nose is very promising for application in human health monitoring, food monitoring, and other fields.

Outdoor Air Quality Monitoring with Enhanced Lifetime-enhancing Cooperative Data Gathering and Relaying Algorithm (E-LCDGRA) Based Sensor Network

The air continues to be an extremely substantial part of survival on earth. Air pollution poses a critical risk to humans and the environment. Using sensor-based structures, we can get air pollutant data in real-time. However, the sensors rely upon limited-battery sources that are immaterial to be alternated repeatedly amid extensive broadcast costs associated with real-time applications like air quality monitoring. Consequently, air quality sensor-based monitoring structures are lifetime-constrained and prone to the untimely loss of connectivity. Effective energy administration measures must therefore be implemented to handle the outlay of power dissipation. In this study, the authors propose outdoor air quality monitoring using a sensor network with an enhanced lifetime-enhancing cooperative data gathering and relaying algorithm (E-LCDGRA). LCDGRA is a cluster-based cooperative event-driven routing scheme with dedicated relay allocation mechanisms that tackle the problems of event-driven clustered WSNs with immobile gateways. The adapted variant, named E-LCDGRA, enhances the LCDGRA algorithm by incorporating a non-beaconaided CSMA layer-2 un-slotted protocol with a back-off mechanism. The performance of the proposed E-LCDGRA is examined with other classical gathering schemes, including IEESEP and CERP, in terms of average lifetime, energy consumption, and delay

Monitoring and Prediction of Indoor Air Quality for Enhanced Occupational Health

The amount of moisture in the air is represented by relative humidity (RH); an ideal level of humidity in the interior environment is between 40% and 60% at temperatures between 18° and 20° Celsius. When the RH falls below this level, the environment becomes dry, which can cause skin dryness, irritation, and discomfort at low temperatures. When the humidity level rises above 60%, a wet atmosphere develops, which encourages the growth of mold and mites. Asthma and allergy symptoms may occur as a result. Human health is harmed by excessive humidity or a lack thereof. Dehumidifiers can be used to provide an optimal level of humidity and a stable and pleasant atmosphere; certain models disinfect and purify the water, reducing the spread of bacteria. The design and implementation of a client-server indoor and outdoor air quality monitoring application are presented in this paper. The Netatmo station was used to acquire the data needed in the application. The client is an Android application that allows the user to monitor air quality over a period of their choosing. For a good monitoring process, the Netatmo modules were used to collect data from both environments (indoor: temperature (T), RH, carbon dioxide (CO2), atmospheric pressure (Pa), noise and outdoor: T and RH). The data is stored in a database, using MySQL. The Android application allows the user to view the evolution of the measured parameters in the form of graphs. Also, the paper presents a prediction model of RH using Azure Machine Learning Studio (Azure ML Studio). The model is evaluated using metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Relative Squared Error (RSE) and Coefficient of Determination (CoD).

An Efficient Wireless Sensor Network Based on the ESP-MESH Protocol for Indoor and Outdoor Air Quality Monitoring

The main aim of this work is to establish a sensor MESH network using an ESP-MESH networking protocol with the ESP32 MCU (a Wi-Fi-enabled microcontroller) for indoor and outdoor air quality monitoring in real time. Each sensor node is deployed at a different location on the college campus and includes sensor arrays (CO2, CO, and air quality) interfaced with the ESP32. The ESP-MESH networking protocol is a low-cost, easy-to-implement, medium-range, and low-power option. ESP32 microcontrollers are inexpensive and are used to establish the ESP-MESH network that allows numerous sensor nodes spread over a large physical area to be interconnected under the same wireless network to monitor air quality parameters accurately. The data of different air quality parameters (temperature, humidity, PM2.5, gas concentrations, etc.) is taken (every 2 min) from the indoor and outdoor nodes and continuously monitored for 72 min. A custom time-division multiple-access (TDMA) scheduling scheme for energy efficiency is applied to construct an appropriate transmission schedule that reduces the end-to-end transmission time from the sensor nodes to the router. The performance of the MESH network is estimated in terms of the package loss rate (PLR), package fault rate (PFR), and rate of packet delivery (RPD). The value of the RPD is more than 97%, and the value of the PMR and PER for each active node is less than 1.8%, which is under the limit. The results show that the ESP-MESH network protocol offers a considerably good quality of service, mainly for medium-area networks.

Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution.

Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.

A GPS Based Unmanned Drone Technology for Detecting and Analyzing Air Pollutants

Air pollution is now becoming a global issue because it directly affects human health, by creating cancer, respiratory and cardiac diseases, and the earth's ecosystem, by causing climate change and depletion of ozone layers. The rapid expansion of civilization and industrialization, leading to hazardous emissions from automobiles, chemical industries, and fossil fuel burning, plays a significant role in air and atmospheric pollution. Unmanned aerial systems and the internet of things-based sensing devices have provided new impetus for automatic air pollution monitoring and analysis. In this paper, an automatic indoor and outdoor air quality monitoring system has been developed using an unmanned aerial vehicle: a drone. The proposed device is constructed from an ultra-strength DJI F450 frame, various gas sensors, a particulate matter detector, and a camera to detect and distinguish air pollution data. The precision and sensitivity of the proposed device have been evaluated by collecting air emissions data from two places in Dhaka, Bangladesh. The drone has been navigated in Uttara and Bashundhara areas to measure the amount of CO, CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , O <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> , SO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , NOx, and dust in indoor and outdoor environments. We obtained higher air pollution concentrations in Uttara than in Bashundhara, as it is an area with more industries and construction sites. The proposed system composed of an uncrewed aerial device and low-cost gas monitoring sensors is expected to create new possibilities in environmental and air pollution monitoring.

Wearable system for outdoor air quality monitoring in a WSN with cloud computing: Design, validation and deployment

Breathing poor-quality air is a global threat at the same level as unhealthy diets or tobacco smoking, so the availability of affordable instrument for the measurement of air pollutant levels is highly relevant for human and environmental protection. We developed an air quality monitoring platform that comprises a wearable device embedding low-cost metal oxide semiconductor (MOS) gas sensors, a PM sensor, and a smartphone for collecting the data using Bluetooth Low Energy (BLE) communication. Our own developed app displays information about the air surrounding the user and sends the gathered geolocalized data to a cloud, where the users can map the air quality levels measured in the network. The resulting device is small-sized, light-weighted, compact, and belt-worn, with a user-friendly interface and a low cost. The data collected by the sensor array are validated in two experimental setups, first in laboratory-controlled conditions and then against referential pollutant concentrations measured by standard instruments in an outdoor environment. The performance of our air quality platform was tested in a field testing campaign in Barcelona with six moving devices acting as wireless sensor nodes. Devices were trained by means of machine learning algorithms to differentiate between air quality index (AQI) referential concentration values (97% success in the laboratory, 82.3% success in the field). Humidity correction was applied to all data.

Influence of an Extreme Saharan Dust Event on the Air Quality of the West Region of Portugal

This paper describes how an extreme Saharan dust event that took place in March 2022 affected the Iberian Peninsula and was noticed not only by the outdoor air quality monitoring stations measuring PM2.5 and PM10 but also by indoor air monitoring systems in Fatima, central Portugal. The observed particulate matter concentrations clearly show the influence that such an event has on the indoor air quality inside buildings and that the magnitude of that influence is also dependent on the specific characteristics of the buildings, mainly the ventilation conditions, as should be expected. Therefore, this study alerts us to the necessity of integrating indoor and outdoor air quality monitoring systems to achieve automated air conditioning systems capable of efficiently controlling both temperature and air cleanliness.

Evaluation of a new low-cost particle sensor as an internet-of-things device for outdoor air quality monitoring

ABSTRACT Many low-cost particle sensors are available for routine air quality monitoring of PM2.5, but there are concerns about the accuracy and precision of the reported data, particularly in humid conditions. The objectives of this study are to evaluate the Sensirion SPS30 particulate matter (PM) sensor against regulatory methods for measurement of real-time particulate matter concentrations and to evaluate the effectiveness of the Intelligent AirTM sensor pack for remote deployment and monitoring. To achieve this, we co-located the Intelligent AirTM sensor pack, developed at Clemson University and built around the Sensirion SPS30, to collect data from July 29, 2019, to December 12, 2019, at a regulatory site in Columbia, South Carolina. When compared to the Federal Equivalent Methods, the SPS30 showed an average bias adjusted R2 = 0.75, mean bias error of −1.59, and a root mean square error of 2.10 for 24-hour average trimmed measurements over 93 days, and R2 = 0.57, mean bias error of −1.61, and a root mean square error of 3.029, for 1-hr average trimmed measurements over 2300 hours when the central 99% of data was retained with a data completeness of 75% or greater. The Intelligent AirTM sensor pack is designed to promote long-term deployment and includes a solar panel and battery backup, protection from the elements, and the ability to upload data via a cellular network. Overall, we conclude that the SPS30 PM sensor and the Intelligent AirTM sensor pack have the potential for greatly increasing the spatial density of particulate matter measurements, but more work is needed to understand and calibrate sensor measurements. Implications: This work adds to the growing body of research that indicates that low-cost sensors of particulate matter (PM) for air quality monitoring has a promising future, and yet much work is left to be done. This work shows that the level of data processing and filtering effects how the low-cost sensors compare to existing federal reference and equivalence methods: more data filtering at low PM levels worsens the data comparison, while longer time averaging improves the measurement comparisons. Improvements must be made to how we handle, calibrate, and correct PM data from low-cost sensors before the data can be reliably used for air quality monitoring and attainment.

On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN

This paper proposed implementing a water and air monitoring system using sensor development and a LoRa Network. To transmit data, a self-made PCB board integrates the terminal sensors with Renesas RX64M MCU and LoRa. There are 16 monitoring point stations for the media experiment. The sensors were used to measure the water and air parameters such as PM2.5, CO2, DO concentration, pH level, temperature, and humidity. In addition, the Grafana system was implemented to present the status and variation in the monitoring parameters in the environmental area. To evaluate the monitoring system, we also collected public information provided by the environmental protection department of the Taiwan government at the same monitoring point for comparison.