Air Quality Monitoring System Research Articles

Design and construction of a home air quality monitoring system

Air pollution is one of the most dangerous problems we face in the world today. It is the cause of many illnesses like asthma, cancer, bronchitis, congenital disabilities and other diseases that affect the immune system of both humans and non-human animals. Among the factors that characterize the quality of air are temperature and humidity and the level of Carbon dioxide in the environment. This reseaerch involves the design and construction of a device that measures the temperature, humidity and CO2 levels in an environment, using a DHT11 and MQ135 sensor. The values were compared with the expected values from a trusted website to determine the accuracy of the device. The device was also used to take the temperature, humidity and CO2 levels of the living room, bedroom, kitchen and balcony in a house to determine the quality of air in the house. The results obtained in the living room and balcony revealed impressive air quality with the temperature and humidity in the safe zone while the level of CO2 was normal. The air quality in the bedroom was acceptable, although it could do with better ventilation if it is to house more people. The kitchen, however, showed high temperatures at certain hours and high humidity as well as CO2 levels. It was concluded that improvements are necessary for the ventilation of the kitchen to improve the quality of air.

Practice of Air Environment Quality Monitoring Data Visualization Technology Based on Adaptive Wireless Sensor Networks

This paper begins by outlining the evolution and current state of traditional wireless sensor networks, followed by an examination of the principle and architecture of wireless sensor networks, as well as the challenges that traditional wireless sensor networks face in environmental monitoring. This paper designs and develops a data visualization system for wireless sensor networks based on the visualization system and the advantages of other visualization systems, after analyzing and comparing the current visualization systems for monitoring wireless sensor networks. A real-time monitoring system for urban air quality based on wireless sensor network is designed to solve the problem that the traditional method of combining manual sampling with laboratory analysis for air quality monitoring is expensive and cannot carry out real-time online monitoring for multiple points at the same time. The system’s implementation is based primarily on air quality monitoring data collected in the Shenyang area for visual display and analysis. The air quality monitoring system proposed in this paper has a complete structure and independent modules and is simple to use. The dynamic adaptability of the monitoring system is greatly improved by utilizing the self-adaptability and self-organization of wireless sensor networks, which brings great convenience to air quality monitoring.

Air Quality in Lombardy, Italy: An Overview of the Environmental Monitoring System of ARPA Lombardia

In this paper, we describe the structure and the features of the air quality and meteorological monitoring system adopted in the Lombardy region in Northern Italy. We are interested in describing which data the Regional Agency for Environmental Protection (ARPA Lombardia) must collect, how this process takes place and how they are disseminated to the public for institutional communication and research purposes. ARPA monitors the atmospheric conditions through a dense ground monitoring network composed mainly by permanent stations, but also by mobile samplers. We describe in a detailed fashion the structure of the network: how many stations the network consists of, their locations, which pollutants and weather events are monitored and with what frequency. Our main objective is to present to an international audience the case study of ARPA Lombardia and the main available public data, explicitly stating the sources of information and how to find them, and encouraging international researchers to deal with the subject. In spite of the significant and extensive efforts made to counteract the phenomenon of air pollution, the air quality recorded in the region is very poor, and the local authorities are struggling to comply with international regulations on the concentration of pollutants in the air, making Lombardy a relevant international case. In addition, we present in a synthetic and descriptive way, without any modeling ambition, some data observed in the last years in Lombardy regarding meteorology and the main pollutants (oxides and particulate matters). The empirical descriptive results have been obtained by analyzing sample data provided by ARPA Lombardia through the same sources described in the sections dedicated to the Agency. From the graphical analysis, it is noticeable that at aggregate (regional) level, the concentrations are affected by significant decreasing trends, but at a rather contained speed. This is particularly true for the concentrations of oxides (NO2 and NOX) in urban and industrial areas. However, particulate matters and ozone show a high persistence in the average concentrations, interrupted only by the alternation of climatic seasons. The data also show that the meteorology of the region does not seem favorable for the improvement of air quality, as the region is characterized by low precipitation, and wind almost everywhere is not very intense. This situation could be induced by the unfavorable geography of the area, which prevents adequate air recycling and facilitates the stagnation of pollutants. We suggest that any public policy intervention aimed at improving the air quality situation in the region should take into account this empirical evidence in the impact assessment phase.

Sistem Monitoring dan Notifikasi Kualitas Udara Dijalan Raya Dengan Platform IOT

Air pollution and air pollution on the highway is a very bad impact on humans because polluted air can cause various kinds of diseases, therefore in this study, a tool was designed that can be used to monitor air quality over long distances, using Wemos. d1 as a liaison between the MQ-135 sensor with the Internet of Things (IoT) platform and the blynk application which is a monitoring system that if the air quality on the highway where many vehicles pass makes air pollution on the highway less healthy so that it threatens the physical health of living things. One of the efforts to overcome air pollution is by measuring the quality of the air on the highway. connected using the IoT platform as an air quality monitoring system by using the internet as a means of providing information presentation about the air quality on the highway. The results of field testing at 2 points in the East Lombok area found that the quality was still moderate where the air quality was in the range of 400-800. But if the air conditioner is Normal, the range is 0-400 and the last is Abnormal air quality, the range is 800 and above.

Internet GIS-Based Air Quality Monitoring and Forecast System for the Indian Region Using FOSS4G

Internet GIS-Based Air Quality Monitoring and Forecast System for the Indian Region Using FOSS4G

Autonomous Multi-Rotor Aerial Platform for Air Pollution Monitoring.

During the last few years, scientists have become increasingly concerned about air quality. Particularly in large cities and industrialised areas, air quality is affected by pollution from natural and anthropogenic sources and this has a significant impact on human health. Continuous monitoring of air quality is an important step in investigating the causes and reducing pollution. In this paper, we propose a new autonomous multi-rotor aerial platform that can be used to perform real-time monitoring of air quality in large cities. The air quality monitoring system is able to cover large areas, with high spatial resolution, even above average buildings, while being relatively low cost. We evaluate the proposed system in several locations throughout a metropolitan city, during different seasons and generate fine-grained heat-maps that display the level of pollution of specific areas based on different altitudes.

Clustering analysis of PM2.5 concentrations in the South Sumatra Province, Indonesia, using the Merra-2 Satellite Application and Hierarchical Cluster Method

<abstract> <p>The air quality monitoring system is the most prominent tool for monitoring air pollution levels, especially in areas where forest fires often occur. The South Sumatra Province of Indonesia is one of the greatest contributors to haze events in Indonesia due to peatlands fires. It does not sufficiently possess a ground monitoring system to cover rural areas, and thus, delayed actions can result in severe air pollution within this region. Therefore, the aim of this current study is to analyze the distribution and classification of PM2.5 observed from 2019 to 2021 within the South Sumatra Province, Indonesia. The acquisition of PM2.5 data was from the Merra-2 Satellite with a spatial resolution of 0.5˚ × 0.625˚ and an hourly interval. The hierarchical cluster analysis (HCA) was applied in this study for the clustering method. The result of the study revealed that the daily mean of PM2.5 levels varied from 5.9±0.01 to 21.3±0.03 μg/m<sup>3</sup>. The study area was classified into three classes: high pollution areas (HPA), moderate pollution areas (MPA) and low pollution areas (LPA), based on the HCA method. The average level of PM2.5 observed in HPA was notably higher, at 16.8±0.02 μg/m<sup>3</sup>, followed by MPA and LPA. Furthermore, this study indicated that the highest level of PM2.5 was found during 2019, with a severe haze event in the study area due to the intensive burning of forests, bush and peatlands. As a whole, the output of this study can be used by authorities for air quality management due to forest fire events in a certain area.</p> </abstract>

Design of Air Quality Monitoring System in Battery Testing Workshop

In the battery test process, the gas data index in the test environment is very important. The system adopts the relevant air index sensor, based on the STM32F103C8T6 minimum system single-chip microcomputer as the core chip, to build the air quality monitoring system design in the battery test workshop. Use various types of sensors related to the main gas contained in the environment in the environment to collect the main gas data in the battery test workshop environment, and display the relevant environmental data in the configured OLED display screen, and send the relevant data to the user through the WIFI module to realize real-time monitoring of the environmental state. This air quality testing system has a wide range of application, excellent performance, and convenient maintenance. It can be applied to the environmental gas test in the test workshop under many special circumstances, which has certain practical value and research significance.

УДОСКОНАЛЕННЯ ДІЮЧОЇ СИСТЕМИ СПОСТЕРЕЖЕНЬ ЗА ЯКІСТЮ АТМОСФЕРНОГО ПОВІТРЯ В М. КИЄВІ У ВІДПОВІДНОСТІ ДО ВИМОГ ЄС

The paper is dedicated to the problems of the current system for atmospheric air quality monitoring which is operated by the national hydrometeorological service of Ukraine in Kyiv. The problems are discussed in the context of implementing the Directives of the European Parliament and the Council on ambient air quality. The improvement of the current monitoring system needs immediate solutions due to the nonconformity to modern requirements not only in Kyiv, but also on the entire Ukrainian territory. The study makes an attempt to define the main steps of network improvement and the optimization of air pollution measurement network using air pollution data from the archives of the Central Geophysical Observatory, pollutants’ emission, land-use/land cover and meteorological data for 2000-2019. We discuss main EU requirements for monitoring sites’ location, atmospheric air pollution and emission data. The analysis showed the necessity for preliminary urban field measurements before the establishment of monitoring sites. Depending on the wind regime, the new measurement network must take into account the prevailing synoptic processes for future understanding how air pollution is transported towards and outwards the urban area. Land-use/ land cover data derived from the Landsat allowed distinguishing residential areas, industrial zones, main roads, water bodies and green areas which is crucial for the selection of suitable places for monitoring sites. Using mentioned principles and requirements, we propose 15 monitoring sites for the improved air quality network in Kyiv. This number of sites are greater than EU Directives claimed because conclusions were made on theoretical estimations without field measurements. The presented list of the monitoring sites needs additional analysis of spatio-temporal pollutants’ distribution in combination with atmospheric modelling. However, the analysis considers actual residential areas and emission sources in the background of the modern wind regime in Kyiv.

Free Hardware based System for Air Quality and CO2 Monitoring

Due to the increase in air pollution, especially in Latin American countries of low and middle income, great environmental and health risks have been generated, highlighting that there is more pollution in closed environments. Given this problem, it has been proposed to develop a system based on free hardware for monitoring air quality and CO2, in order to reduce the levels of air pollution in a closed environment, improving the quality of life of people and contributing to the awareness of the damage caused to the environment by the hand of man himself. The system is based on V-Model, complemented with a ventilation prototype implemented with sensors and an application for its respective monitoring. The sample collected in the present investigation was non-probabilistic, derived from the reports of air indicators during 15 days with specific schedules of 9am, 1pm and 6pm. The results obtained indicated that the air quality decreased to 670 ppm, as well as the collection time decreased to 5 seconds and finally the presence of CO2 was reduced to 650 ppm after the implementation of the system, achieving to be within the standards recommended by the World Health Organization.

An intelligent fuzzy and IoT-aware air quality prediction and monitoring system using CRF and Bi-LSTM

Recently, air pollution has been increasing drastically in the majority of metropolitan cities around the world. This is necessary to reduce air pollution, and we propose a new air quality prediction system to predict air quality and pollution levels in different seasons in Beijing, China. The proposed air quality model applies a preliminary data preprocess to get exact data, a newly proposed conditional random field (CRF), and a fuzzy rule-based data grouping algorithm (CRF-FRDGA) to group the data according to the different seasonal data by applying the necessary rules, the standard bidirectional LSTM (Bi-LSTM) for performing an effective classification and prediction process. The PM2.5 concentration in Beijing, China, is forecasted season-wise for the next five years. Various experiments have been done to prove the capability of the proposed air quality prediction system and proved better than the existing works in prediction accuracy.

An Intelligent Fuzzy and IoT aware Air Quality Prediction and Monitoring System using CRF and Bi-LSTM

An Intelligent Fuzzy and IoT aware Air Quality Prediction and Monitoring System using CRF and Bi-LSTM

Design of a Low-Cost Air Quality Monitoring System Using Arduino and ThingSpeak

The impact of daily emissions of gaseous and particulate pollutants of machines and industries on human health and the environment has attracted increasing concerns. This impact has significantly led to a notable increase in mortality in the highly industrialized zones. Therefore, monitoring air quality and creating public awareness are important for a safer future, which led the governments globally to invest multi-billion in policymaking and solution stratification to address the problem. This study aims to design a real-time Internet of Things low-cost air quality monitoring system. The system utilizes air quality and carbon monoxide sensors for monitoring gaseous pollutants. Moreover, the system utilizes an Arduino Nano development board equipped with a WiFi module to effectively send readings to a ThingSpeak online channel platform for instantaneous and real-time display of air quality. The ThingSpeak uses HTTP protocols to send emails in raising awareness of poor air quality. The level of concentration is monitored graphically through channels with the help of ThingSpeak to aid remote communication. A threshold value is set. Thus, when pollutants have become unhealthy and harmful, the system trips off an alarm, and e-mail notifications are sent to the officials. The results have shown that the work was successfully implemented a design of a low-cost air quality monitoring system using Arduino and ThingSpeak, showing that an air quality system can be implemented using a low-cost technology, Arduino and ThingSpeak.

A Novel Predictor for Exploring PM2.5 Spatiotemporal Propagation by Using Convolutional Recursive Neural Networks

<p>The spread of PM2.5 pollutants that endanger health is difficult to predict because it involves many atmospheric variables. These micro particles could spread rapidly from their source to residential areas, increasing the risk of respiratory disease if exposed for long periods. However, the existing prediction systems do not take into account the geographical correlation among neighboring nodes spatially and temporally resulting in loss of important information, lack of PM2.5 propagation resolution, and lower forecasting accuracy. In this paper, a novel scheme is proposed to generate propagation heat maps of PM2.5 prediction by using spatiotemporal datasets. In this scheme, the deep learning model is implemented to extract spatiotemporal features on these datasets. This research was conducted by using the dataset of air quality monitoring systems in Taiwan. Moreover, the robust model based on the convolutional recursive neural network is presented to generate the propagation maps of PM2.5 concentration. This study develops an intelligence-based predictor by using Convolutional Recursive Neural Network (CRNN) model for predicting the PM2.5 propagation with uncertain spread and density. It is also one of key technologies of software and hardware co-design for massive Internet of Things (IoT) applications. Finally, the proposed model the proposed model provides accurate predictive results over time by taking into account the spatiotemporal relationship among sensory nodes in order to give a prediction solution for the massive IoT deployment based on green communication.</p> <p> </p>

Characterization of roadside air pollutants: An artery road of Lanzhou city in northwest China

Due to the lack of air and traffic monitoring data in China, this study employed a remote sensing (RS) system and air quality monitoring sensors to investigate the air pollutant concentrations, traffic profiles and meteorological data in order to characterize roadside pollutant concentrations and identify their influence factors for a major road in Lanzhou. The results suggested that the temporal variations of concentrations of SO2, NO2, PM2.5, PM10 and CO peaked at 5:00-7:00 while O3 peaked at 17:00. The concentrations of roadside air pollutants were lower on weekdays than weekends except O3. The concentrations of PM2.5 and PM10 declined with the height of the monitoring sites increased, peaking at 2-meter height whereas SO2, NO2, CO, and O3 reached their highest concentrations at 4-meter height. The concentrations of roadside NO2 and CO show positive correlations with the volume of heavy-duty trucks (HDTs) and light-duty passenger vehicles (LDPVs), respectively. This study provided scientific support to the establishment of roadside air quality monitoring systems and the assessment of the environmental and health impact from vehicle emissions.

Air quality monitoring system for IOT based smart city applications

It is critical to keep essential air pollutants within the World Health Organization's (WHO) prescribed levels in order to counteract the rising number of people killed by the accompanying health concerns. This is particularly true in enclosed spaces, wherein poor ventilation can amplify the impacts of pollutants. Knowing the amount of contaminants in the air would be a good starting point for taking measures to mitigate. This paper proposes a domestic air pollution monitoring system based on the Internet of Things framework. Two CO2 and PM2.5 sensor that are essential for air pollution monitoring with adjusted air quality forecasting are being sent to the cloud via a LoRaWAN protocol compatible gateways that connects the sensor to the server side of the network. The designed Web-based user interface dashboards enable the users to query the system and retrieve data as well as analytical data. Baseline techniques are designed and deployed to make it easier to configure triggers for each sensor node and transmit notifications when a measured parameter surpasses a defined threshold value.

Air Quality Monitoring in Chittagong by a Low Cost IoT Based Air Quality Monitoring System

Air is an essential element of the environment we live in. Unfortunately, this air is polluted incessantly in many ways. With the advancement of technology, to make life easier, to live with full comfort, this essential part of the environment is being polluted without any interruption. This constant pollution poses a significant threat to the ecosystem as well as to all the living beings on this planet. To make this planet a suitable place for living, for the betterment of all the living beings of this nature, this pollution should be checked. To create awareness, at first, it is essential to show the current condition of the atmosphere, so that people will see the extent to which their activities affect the environment. In this work, we developed an Internet-of-Things (IoT) based low-cost air quality monitoring system to monitor the air quality of Chittagong, a district located in the south-eastern region of Bangladesh. Our proposed system is not only cost-efficient, it also monitors the air quality in real-time. The air quality measurement was taken based on Parts per Million (PPM) metrics and then analyzed. The proposed method provides accurate results.

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.

Air Quality and Active Transportation Modes: A Spatiotemporal Concurrence Analysis in Guadalajara, Mexico

The protection of pedestrians, cyclists, and public transportation passengers from environmental pollution is a global concern. This study fills the gap in the existing knowledge of temporal exposure to air pollution in Latin American metropolises. The paper proposes a methodology addressing the relationship between two objects of study, i.e., the users of active modes of transport and air quality. This new methodology assesses the spatiotemporal concurrence of both objects with statistical analysis of large open-access databases, to promote healthy and sustainable urban mobility. The application of the empirical methodology estimated the number of users of active transportation modes exposed to poor air quality episodes in the Guadalajara metropolitan area (Mexico) in 2019. The study considered two pollutants, ozone (O3) and particulate matter (PM10), and two active modes, cycling and bus rapid transit (BRT). Spatiotemporal analyses were carried out with geographic information systems, as well as with numeric computing platforms. First, big data were used to count the number of users for each mode within the area of influence of the air quality monitoring stations. Second, the number of air pollution episodes was obtained using the air quality index proposed by the Environmental Protection Agency (USA) on an hourly basis. Third, the spatiotemporal concurrence between air quality episodes and active mode users was calculated. In particular, the air quality monitoring data from the Jalisco Atmospheric Monitoring System were compared to users of the public bicycle share system, known as MiBici, and of a bus rapid transit line, known as Mi Macro Calzada. The results showed that the number of cyclists and BRT passengers exposed to poor air quality episodes was considerable in absolute terms, that is, 208,660 users, while it was marginal when compared to the total number of users exposed to better air quality categories in the study area, who represented only 10%. To apply the results at the metropolitan scale, the spatial distribution of the air quality monitoring system should be improved, as well as the availability of data on pedestrians and conventional bus passengers.

Constant approximation for opportunistic sensing in mobile air quality monitoring system

Monitoring air quality plays a critical role in the sustainable development of developing regions where the air is severely polluted. Air quality monitoring systems based on static monitors often do not provide information about the area each monitor represents or represent only small areas. In addition, they have high deployment costs that reflect the efforts needed to ensure sufficient quality of measurements. Meanwhile, the mobile air quality monitoring system, such as the one in this work, shows the feasibility of solving those challenges. The system includes environmental sensors mounted on buses that move along their routes, broadening the monitoring areas. In such a system, we introduce a new optimization problem named opportunistic sensing that aims to find (1) optimal buses to place the sensors and (2) the optimal monitoring timing to maximize the number of monitored critical regions. We investigate the optimization problem in two scenarios: simplified and general bus routes. Initially, we mathematically formulate the targeted problem and prove its NP-hardness. Then, we propose a polynomial-time 12-, e−12e−1-approximation algorithm for the problem with the simplified, general routes, respectively. To show the proposed algorithms’ effectiveness, we have evaluated it on the real data of real bus routes in Hanoi, Vietnam. The evaluation results show that the former algorithm guarantees an average performance ratio of 72.68%, while the latter algorithm achieves the ratio of 63.87%. Notably, when the sensors can be on (e.g., enough energy) during the whole route, the e−12e−1-approximation algorithm achieves the approximation ratio of (1−1e). Such ratio, which is almost twice as e−12e−1, enlarges the average performance ratio to 78.42%.