Pollution Monitoring System Research Articles

Air Pollution Monitoring System with Prediction Abilities Based on Smart Autonomous Sensors Equipped with ANNs with Novel Training Scheme

The paper presents a concept of an air pollution monitoring system with prediction abilities, based on wireless smart sensors, that takes into account local conditions (microclimate) prevailing in particular areas of the city. In most cases reported in the literature, artificial neural networks (ANNs) are used to predict future pollution levels. In existing solutions of this type, ANNs are trained with generalized datasets common for larger areas, e.g., cities. Our investigations show, however, that conditions may strongly differ even between particular streets in the city, which may impact prediction quality. This results from varying density of urban development, different levels of insolation, airiness, amounts of greenery, etc. As a result, with similar values of ANN input signals, such as current pollution levels, temperature, pressure, etc., the results of the prediction may differ significantly from reality. For this reason, we propose an innovative solution, in which particular sensors are equipped with miniaturized low-power ANNs, trained with datasets gathered directly from their closest environment, without a need for the obtaining of such data from a base station. This may simplify the installation and maintenance process of a network of such sensors. In a further part of this work, we dealt with solutions that enable the reduction of the computational complexity of ANNs in the case of their implementation on specialized integrated circuits. We propose replacing the most complex mathematical operations used in the learning algorithm with simpler solutions. A prototype chip containing the main blocks of such an ANN was also designed.

Evaluation of traffic noise pollution using geographic information system and descriptive statistical method: a case study in Mashhad, Iran.

Environmental consequences and the epidemiologic results of noise pollution have chronic effects leading to widespread complications in the long run. As far as we know, there are a few studies for pollution monitoring and control systems in comparison with other environmental pollutants. One of the largest metropolitan cities located in Iran is Mashhad city as known as one of the biggest religious cities in the world. Different properties of this city including historical, industrial, and religious draw thousands of visitors to Mashhad, yearly. This fact motivates us to contribute to the concept of noise pollution in streets and sidewalks around the Holy Shrine, namely, Imam Reza. In this regard, different measurements using geographic information system (GIS) and descriptive statistical methods were conducted for our case study in Mashhad, Iran. All measurements and records were done during the peak of morning crowd (10–12 AM) and evening crowd (4–6 PM) on both sidewalks of each street around the Holy Shrine. This study showed that the pollution in the evening time span (4–6 PM) has the maximum level of noise. Among all streets in our case study in Mashhad, Iran, Tabarsi street has the most amount of noise pollution with a mean of 78 dB(A) for the mean intensity for each point, and Imam Reza street has the minimum amount of pollution with a mean of 72.75 dB(A). Our findings from the temporal perspective analysis confirm that the noise pollution peaks in the evening, when weather conditions are favorable. From the spatial perspective analysis, the most intensive noise pollution was observed around residential and accommodation land uses, which have the highest number of arterial routes towards the Holy Shrine.

Large Scale Air Pollution Monitoring Using Static Multihop Wireless Sensor Networks

In the proposed paper, an effective system for a large-scale air pollution monitoring on a live basis is developed and tested. It is an Audrino based platform equipped with pre-calibrated sensors to sense gases like sulphur dioxide (SO2), carbon monoxide (CO), ammonia (NH3), and particulate matter (PM10) in the air. Audrino board with the gas sensors, global system for mobile communications (GSM) and ZigBee module forms the wireless sensor motes used for the field deployment. Wireless sensor network built with ZigBee links can be scaled up using the GSM connectivity to interface with the external world. Air pollution monitoring is performed using a system of sensor nodes with the help of wireless communication via a ZigBee protocol. We proposed a static wireless sensor network to supervise air contamination level, and a multi-hop algorithm is implemented over ZigBee WSN. Designed low-cost WSN based prototype is tested under various atmospheric conditions which had provided stable and dependable data of air contaminants.

Recommendations of Controlling and Preventing Acute Health Risks of Fine Particulate Matter Pollution - China, 2021.

The task force has comprehensively reviewed efforts for air pollution prevention and control, the acute health effects of fine particles (PM2.5), and the health benefits of air pollution prevention and control in China. It has been found that the overall prevention and control of ambient PM2.5 pollution in China has made remarkable progress in recent years. However, it still remains at a relatively high level. Short-term exposure to ambient PM2.5 significantly increases the mortality and morbidity risk of Chinese residents, resulting in changes to levels of relevant biological markers. Prolonged PM2.5 heavily polluted weather greatly increases the risk of cardiovascular disease morbidity and mortality. Among chemical composition of PM2.5, carbon-containing components, some inorganic salts, and heavy metals are linked with the health impacts. The health risks of PM2.5 pollution are higher for children, the elderly, and patients with cardiovascular or respiratory diseases than for the general population because the former groups are vulnerable subpopulations. The implementation of air pollution prevention and control policies has significantly improved human health. The implementation of personal protective equipment can significantly reduce the health damage caused by short-term exposure to ambient PM2.5 pollution. Based on scientific evidence of PM2.5 pollution and acute health risks in China, the following three recommendations are proposed. 1) The policy recommendations for the prevention and control of ambient PM2.5 pollution include the following: to continuously strengthen the widespread use and efficient development of clean energy; to further promote industrial upgrading; to focus on the control of transportation pollution; to keep improving the modernization system of air pollution control; to formulate and refine relevant standards for air quality gradually; and to estimate the effects and health benefits after the implementation of clean air actions, and relevant policies. 2) Prevention of ambient PM2.5 pollution and protection of public health recommendations include the following: to strengthen the release of air pollution monitoring and relevant information; to strengthen awareness of air pollution hazards; to clarify the guidance and recommendations for protecting population health from air pollution; and to strengthen the health protection of population vulnerable to ambient air pollution. 3) Recommendations for research on health risks of air pollution include the following: to strengthen research on air pollutant monitoring technology and monitoring system based on the promotion of accurate exposure assessment; to systematically carry out full-spectrum identification and correlation studies of air pollutants and health effects; to conduct studies on key toxic components and early biomarker inventory of air pollution health effects; to discover the toxicity mechanisms of the key toxic components of air pollutants; to carry out research on population health risk assessment and early warning of combined exposure to air pollutants; and to execute comprehensive studies on the health and economic benefits of pollution and carbon reduction under the national strategies of carbon neutrality and beautiful China.

Design and Implementation of Ship Water Pollution Supervision and Service Information System

In order to improve the ship pollutant discharge monitoring system, control the ship pollutant discharge, establish a more convenient and efficient information communication channel for ship pollutant discharge and receiving services, a ship water pollution discharge system is designed and implemented. This system is divided into three workbenches, namely ship workbenches, enterprise workbenches and supervision workbenches. It is developed based on the front-end and back-end separation mode. The front-end uses Vue framework to implement page function, and Element UI to complete page UI design, Mapbox to display maps, and ECharts component to draw charts. The back-end is implemented through the Spring Boot framework. Redis is used for data caching to improve concurrency and provide data interfaces. Spring Security + JWT is used to achieve single sign on and permission control. The Nginx service gateway is used to achieve service load balancing. MySQL database is used to storage the water pollution discharge information. AIS historical data storage is realized through the distributed database system of Doris cluster.

Advanced Real-Time Pollutant Monitoring Systems for Automatic Environmental Management of Construction Projects Focusing on Field Applicability

AbstractTo minimize damage to people due to pollutants generated from construction work, it is essential to manage environmental performance by maintaining pollutant emissions below regulation stan...

Vehicle pollutant control system using IOT

Environmental pollution is a constant problem. Since vehicle emissions are the main cause of pollution, this approach can be a preventive measure. The purpose of the paper is to implement a vehicle pollution monitoring system based on the Internet of Things (IoT). Sensors connected to the Arduino Uno monitor the level of pollutants in the vehicle's exhaust gases. If the measured value exceeds the threshold value, the vehicle owner will be issued a warning message two or three times as a warning, and if the owner refuses to repair it, the message will be sent to the authorities. Finally, the data is stored in the cloud for further processing. The main goal is to create an innovative and time-saving method that will help solve the problem of environmental pollution.

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.

Best Available Technology for P-Recycling from Sewage Sludge—An Overview of Sewage Sludge Composting in Austria

In order to close the phosphorus cycle in the long term, efficient recycling processes are necessary to ensure that this critical nutrient can be returned to arable land. Sewage sludge recycling is of particular importance due to the relatively high phosphorus content of sewage sludge. In this article, the current recycling paths of Austrian sewage sludge are highlighted, focusing on the advantages and limitations of sewage sludge composting. In addition to nutrient contents, pollutant loads were also analyzed in order to also discuss the limitations of this recycling pathway. Therefore, data from Austrian composting plants with focus on sewage sludge are used. The results show that the currently relevant pollutants (heavy metals) are predominantly below the limits prescribed for recycling and spreading on arable land. However, in order to decide on a recycling path at an early stage, a pollutant monitoring system must be in place. Due to pollution, mono-incineration with subsequent phosphorus recovery is also currently being discussed in Austria. Mono-incineration can represent an important component of sewage sludge disposal, because some sewage sludges are not suitable for composting due to potential environmental hazards. Therefore, it is important that evidence-based limit values and measures for the reduction in pollutants for input sources are determined.

IoT enabled environmental toxicology for air pollution monitoring using AI techniques

In past decades, the industrial and technological developments have increased exponentially and accompanied by non-judicial and un-sustainable utilization of non-renewable resources. At the same time, the environmental branch of toxicology has gained significant attention in understanding the effect of toxic chemicals on human health. Environmental toxic agents cause several diseases, particularly high risk among children, pregnant women, geriatrics and clinical patients. Since air pollution affects human health and results in increased morbidity and mortality increased the toxicological studies focusing on industrial air pollution absorbed by the common people. Therefore, it is needed to design an automated Environmental Toxicology based Air Pollution Monitoring System. To resolve the limitations of traditional monitoring system and to reduce the overall cost, this paper designs an IoT enabled Environmental Toxicology for Air Pollution Monitoring using Artificial Intelligence technique (ETAPM-AIT) to improve human health. The proposed ETAPM-AIT model includes a set of IoT based sensor array to sense eight pollutants namely NH3, CO, NO2, CH4, CO2, PM2.5, temperature and humidity. The sensor array measures the pollutant level and transmits it to the cloud server via gateways for analytic process. The proposed model aims to report the status of air quality in real time by using cloud server and sends an alarm in the presence of hazardous pollutants level in the air. For the classification of air pollutants and determining air quality, Artificial Algae Algorithm (AAA) based Elman Neural Network (ENN) model is used as a classifier, which predicts the air quality in the forthcoming time stamps. The AAA is applied as a parameter tuning technique to optimally determine the parameter values of the ENN model. In-order to examine the air quality monitoring performance of the proposed ETAPM-AIT model, an extensive set of simulation analysis is performed and the results are inspected in 5, 15, 30 and 60 min of duration respectively. The experimental outcome highlights the optimal performance of the proposed ETAPM-AIT model over the recent techniques.

Industrial Pollution Monitoring using LABVIEW and Arduino

Abstract: With the increase in the number of industries in the world today, it is important to keep the our environment healthy and no toxic for living creatures. This project concentrates on designing a pollution monitoring system for industries that would be detecting the toxic gases concentration in the atmosphere that is given out by chemical, paper, ammonia, etc producing industries. The system is designed to continuous monitor theconcentration of gases such as LPG, CO, Sulphur, chlorine etc. and warns if increasing level of toxic level of gases are detected. The values of the concentration of gases are logged into the excel file for future reference and perform analysis to understand the pattern of ejecting gases. This project uses Gas sensors and Arduino for detection and transmission of the data to Lab-VIEWsoftware for further process. Index Terms: Lab view, Gas sensors, monitoring, analysis,safety device

Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and Machine Learning Algorithms

Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R2 reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservoir.

Retraction Note: Data mining-based air pollution characteristics and real-time monitoring of college students’ physical and mental health

Due to the significant impact of air pollution on visibility, it is also the most visible environmental problem for the public. This paper analyzes the application scenarios of data mining in the air pollution monitoring system, combined with the target of air pollution anomaly detection, mainly researches classification algorithms and outlier detection algorithms, and proposes an air pollution feature detection method based on data mining. A large number of experiments were carried out before the system integration to verify its effectiveness. Based on the abovementioned new architecture, this paper designs and implements an air pollution real-time monitoring system, which can display air pollution data in real time through rich charts, and integrates and applies air pollution anomaly detection methods to the system’s alarm module. The system can help data center managers monitor the air pollution in the data center and notify the managers to check the atmospheric abnormalities in time. In this article, data mining is also applied to the real-time monitoring of college students’ physical and mental health. A real-time monitoring system is designed for college students’ physical and mental health. A new system architecture is proposed through frequent data push and data IO scenarios, which can effectively monitor the physical and mental health of college students. In this article, data mining technology is used to monitor the characteristics of air pollution and the physical and mental health of college students in real time, which provides a new method for the treatment of air pollution and the protection of the physical and mental health of college students.

An Arrow-Curve Path Planning Model for Mobile Beacon Node Aided Localization in Air Pollution Monitoring System IoT

In wireless sensor networks, it is crucial to support the collected data of sensor nodes with position information. One of the promising ways to acquire the position of unknown nodes is using a mobile anchor node that traverses throughout the network, stops at determined points, and sends its position to aid in obtaining the location of other unknown nodes. The main challenge in using mobile anchor nodes lies in designing the path model with the highest localization accuracy, shortest path length, full coverage area, and minimal power consumption. In this paper, a path model named the Arrow-Curve path model is proposed for mobile node aided localization. The proposed path model can effectively localize all the static unknown sensor positions in the network field with high positioning accuracy and low power consumption while pledging full coverage area. The sensor network is implemented using MATLAB simulation and MCU node in both static unknown nodes and the mobile anchor node. The realtime environment guarantees a realistic environmental model with reliable results. The path model is implemented in realtime in indoor and outdoor environments and compared to the H-Curve path model using a trilateration technique. The results show that the suggested path model achieves better results compared to H-Curve model. The proposed path model achieves an average position error less than that of H-Curve by 10.6% in a simulation environment, 5% in an outdoor realtime environment, and 9% in an indoor realtime environment, and it decreases power consumption by 62.65% in the simulation environment, 50% in the outdoor realtime environment, and 75% in the realtime environment in indoor compared to H-Curve.

IoT-based air pollution monitoring and data analytics using machine learning approach

Air pollution poses significant risks to both the environment and human health, making real-time monitoring essential for effective mitigation. This paper presents an IoT-based air pollution monitoring system that utilizes sensor networks to collect real-time air quality data on pollutants like PM2.5, CO, and NO₂. The data is transmitted via wireless communication to a cloud platform for analysis. Machine learning algorithms, including decision trees and support vector machines, are applied to predict future pollution trends and detect anomalies. The system’s architecture, from sensor deployment to data analytics, is outlined, highlighting its scalability and adaptability. Experimental results from a 30-day urban deployment demonstrate the system’s ability to capture pollution levels and provide accurate forecasts. By integrating IoT and machine learning, the system offers a cost-effective, real-time solution for monitoring and predicting air pollution, supporting urban planning and public health initiatives.

IoT Based Automatic Air Pollution Monitoring and Purification System

Abstract: Degradation of air quality, like climate change and global warming, has become an all-encompassing existential hazard to humanity and natural life. Exposure to severely polluted air on a regular basis causes pulmonary disorders and contributes to severe allergies and asthma. According to studies, more than 10 million people die each year as a result of irregularities produced directly or indirectly by air pollution. The work of Lelieveld et al. [1] sheds light on the gravity of the problem. It is estimated that by 2050, the worldwide premature mortality from air pollution will exceed 6.6 million fatalities per year (358000 from ozone, the rest from PM 2.5) [1]. As a result, we decided to focus our study on improving indoor air quality. Despite the fact that there are numerous indoor air purifiers on the market, their cost belies their effectiveness, and the effective ones are far too expensive for working-class people to afford [2]. In order to address this issue, we created an automated Internet of Things (IoT) based air filtration system that uses an automated air purifier which is triggered when air quality falls below WHO criteria. Our initiative intends to improve indoor air quality by utilizing the most cost-effective and efficient modules available. Keywords: Indoor Air Pollution, Air Purifier, IAQ, Sharp Dust Sensor GP2Y1010AU0F, IoT, Particulate Matter (PM), HEPA Filter

Air Pollution Monitoring System Based on IoT with Cloud

Air pollution kills about seven million people worldwide every year. In Indonesia itself, especially the city of Jakarta, Jakarta's air quality has been ranked as the tenth worst in the world. The purpose of the research is to design an air pollution monitoring system that can help the community to know the level of pollution in their area. The system development method uses a system development life cycle, namely the waterfall. The evaluation method uses blackbox testing for the system and eight golden rules for the user interface. The result achieved is a system that can monitor IoT-based air pollution with the cloud. This system uses several technologies such as Google cloud platform which consists of IoT Core, Pub Sub, Firestore Database, and Google Cloud Function. The MQ 135 temperature sensor and ESP 32 module are also used to be able to read air pollution conditions and send it to the cloud. The conclusion of this study is that the system created can be easily accessed by users to be able to determine IoT-based air quality.

Associations of Air Pollution and Pediatric Asthma in Cleveland, Ohio

Air pollution has been associated with poor health outcomes and continues to be a risk factor for respiratory health in children. While higher particulate matter (PM) levels are associated with increased frequency of symptoms, lower lung function, and increase airway inflammation from asthma, the precise composition of the particles that are more highly associated with poor health outcomes or healthcare utilization are not fully elucidated. PM is measured quantifiably by current air pollution monitoring systems. To better determine sources of PM and speciation of such sources, a particulate matter (PM) source apportionment study, the Cleveland Multiple Air Pollutant Study (CMAPS), was conducted in Cleveland, Ohio, in 2009–2010, which allowed more refined assessment of associations with health outcomes. This article presents an evaluation of short-term (daily) and long-term associations between motor vehicle and industrial air pollution components and pediatric asthma emergency department (ED) visits by evaluating two sets of air quality data with healthcare utilization for pediatric asthma. Exposure estimates were developed using land use regression models for long-term exposures for nitrogen dioxide (NO2) and coarse (i.e., with aerodynamic diameters between 2.5 and 10 μm) particulate matter (PM) and the US EPA Positive Matrix Factorization receptor model for short-term exposures to fine (<2.5 μm) and coarse PM components. Exposure metrics from these two approaches were used in asthma ED visit prevalence and time series analyses to investigate seasonal-averaged short- and long-term impacts of both motor vehicles and industry emissions. Increased pediatric asthma ED visits were found for LUR coarse PM and NO2 estimates, which were primarily contributed by motor vehicles. Consistent, statistically significant associations with pediatric asthma visits were observed, with short-term exposures to components of fine and coarse iron PM associated with steel production. Our study is the first to combine spatial and time series analysis of ED visits for asthma using the same periods and shows that PM related to motor vehicle emissions and iron/steel production are associated with increased pediatric asthma visits.

Development of a low-cost outdoor carbon monoxyde analyser applied to the city of Oran, Algeria

In Algeria, the alternative to the absence of these air pollution measurement networks can come from the recent development of electrochemical sensor technologies for air quality monitoring which arouses a certain interest because of their miniaturization, low energy consumption and low cost.&#x0D; We developed a low-cost outdoor carbon monoxide analyzer called APOMOS (Air pollution Monitoring System) based on electrochemical sensor managed by microcontroller. In order to validate the APOMOS system, the recorded measurements are compared with measurements taken by a conventional analyzer.&#x0D; Comparison of the measurements resulting from conventional analyzer and those resulting from the APOMOS system gives a coefficient of determination of 98.39 %.

Examining land surface temperature and relations with the major air pollutants: A remote sensing research in case of Tehran

BackgroundUrban air pollution is a dynamic mixture of Land Surface Temperature (LST), gases, particulate matter with daily and seasonal changes due to anthropogenic activities, Land-use Land-cover (LULC) transformations, and climatic conditions. The relationship between urban biophysical and thermal conditions, and LULC is generally known; however, the absence of a dense network of land-based meteorological stations is an obstacle to the comparison of LST to Major Air Pollutants (MAP). MethodThis research proposes investigation of the relationships between LST derived by Sentinel-3 SLSTR, MAP derived by Sentinel-5 Precursor, and air pollution monitoring system stations in Tehran province, Iran. The method is designed in a moving average model with the use of a Python application programming interface, geographical information system, and remote sensing. ResultThe mean concentration of the Particulate Matter (PM), Sulfur Dioxide (SO₂), and Nitrogen Dioxide (NO₂) are mainly in the Tehran metropolis and the core urban area. A negative correlation was noted between the PM₂.₅, SO₂, NO₂, and altitude. Additionally, increased altitude negatively affects LST, Carbon Monoxide (CO), and Ozone (O₃) values; whereas, CO and O₃ have positive correlations with LST, representing the mutual impacts of LST, CO, and O₃ values in Tehran province.