Real-time Air Quality Monitoring System Research Articles

Implementation of a sustainable real-time air quality monitoring system using the Internet of Things for Kaduna metropolis, Nigeria

Implementation of a sustainable real-time air quality monitoring system using the Internet of Things for Kaduna metropolis, Nigeria

Development of a Smart Air Quality Monitoring System Using Wireless Sensors

With the fast-growing urbanization and increased industrialization around cities in Nigeria, the quality of the air we breathe has become a paramount concern. There is a need for an accurate, real-time air quality monitoring system to monitor and evaluate the air quality we inhale. This article presents the development of a Smart Air Quality Monitoring System (SAQMS) utilizing wireless sensor technology. The SAQMS was designed to provide a comprehensive and intelligent solution for monitoring air quality index, including but not limited to particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon monoxide (CO), temperature and humidity. Leveraging wireless sensor nodes and advanced IOT, the system offers real-time data collection and analysis, enabling immediate response to environmental changes. Key features of the SAQMS include a network of wireless sensors (MQ 135 and DHT11) strategically deployed across targeted areas, data transmission through secure communication protocols, cloud-based data storage and processing, and a user-friendly interface for data visualization and interpretation. As we confront the challenges of air pollution and its impact on human well-being, the development of the SAQMS presents a significant step towards informed decision-making and a more sustainable future as the experiment shows our developed system can detect air quality in parts per million (ppm), with other environmental factors such as temperature and relative humidity.

Spatio-attention embedded recurrent neural network for air quality prediction

Predicting the air quality index (AQI) has been regarded as a critical problem for environmental control management. Many factors over time and space may relate to the diffusion of pollutants. In other words, there exist very intricate spatio-temporal interactions among the characteristic for revealing diffusion of pollutants. Recently, some relevant works studied the topic of AQI prediction considering spatial and temporal correlations simultaneously, but most of them either ignore geospatially topological structures to learn spatio-temporal dependency or utilize sub-modules separately to encode the spatial and temporal information. Unfortunately, ignoring geospatially topological structures or correlations among spatial properties and temporal dependencies leads that the AQI prediction model cannot deal with the prediction task well. Although few existing works utilize an integrated model to encode the spatial and temporal information with geospatially topological structures, these works focus only on catching inter-station relationships among stations, which only can reflect the spatio-temporal dependency. In this paper, we propose a spatio-attention embedded recurrent neural network, called SpAttRNN, for air quality prediction by comprehensively leveraging dynamic spatio-temporal correlations among monitoring stations to tackle the intricate spatio-temporal interactions. To capture spatial properties, a self-loop-normalized adjacency matrix is utilized to enhance graph-based attention cells to learn the relationships between monitoring stations. Moreover, the proposed graph-based attention cell is embedded into a recurrent neural network to learn dynamic spatio-temporal correlations from multiple monitoring stations simultaneously. We conducted comprehensive experimental evaluations on two real-world air quality datasets collected from Beijing’s real-time air quality monitoring system. The results demonstrate that our method outperforms other state-of-the-art methods significantly. In particular, SpAttRNN delivers up to 15% improvement in terms of the mean square error (MAE) on PM2.5 prediction compared with other existing methods.

Technical Solution for a Real-Time Air Quality Monitoring System

This article will present a simple technical solution for a low-power and real-time air quality monitoring system. The whole package of software and hardware technical solutions applied for recording, transmitting and analyzing data is briefly described. This original monitoring system integrates a single chip microcon-troller, several dedicated air pollution surveillance sensors (for PM10, PM2.5, SO2, NO2, CO, O3, VOC, CO2), a LoRaWAN communication module and an online platform. This system was tested and applied under real field conditions. Depending on the measured values, it provides alerts, or, it can lead to the re-placement of specific components in the exhaust equipment. This article will pre-sent some experimental results, validated also by official measurements of government operated air quality stations.

Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyber-physical Systems and Mobile Computing Technologies

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

무선네트워크기반 공기질 실시간 모니터링 시스템

무선네트워크기반 공기질 실시간 모니터링 시스템

Air pollution monitoring and GIS modeling: a new use of nanotechnology based solid state gas sensors

Air pollution is a serious problem in thickly populated and industrialized areas in Thailand, especially in Bangkok. The air pollution in Bangkok is abundant, especially in areas where pollution sources and the human population are concentrated. Economic growth and industrialization are proceeding at a rapid pace, accompanied by increasing emissions of air polluting sources. Furthermore, though th variety and quantities of polluting sources have increased dramatically, the development of a suitable method for monitoring the pollution causing sources has not followed at the same pace. Environmental impacts of air pollutants have impact on public health, vegetation, material deterioration etc. To prevent or minimize the damage caused by atmospheric pollution, suitable monitoring systems are urgently needed that can rapidly and reliably detect and quantify polluting sources for monitoring by regulating authorities in order to prevent further deterioration of the current pollution levels. Consequently, it is important that the current real-time air quality monitoring system, controlled by the Pollution Control Department (PCD), should be adapted or extended to aid in alleviating this problem.Nanotechnology has been applied to several industrial and domestic fields, for example, applications for gas monitoring systems, gas leak detectors in factories, fire and toxic gas detectors, ventilation control, breath alcohol detectors, and the like. Here we report an application example of studying air quality monitoring based on nanotechnology ‘solid state gas sensors’.So as to carry out air pollution monitoring over an extensive area, a combination of ground measurements through inexpensive sensors and wireless GIS will be used for this purpose. This portable device, comprising solid state gas sensors integrated to a Personal Digital Assistant (PDA) linked through Bluetooth communication tools and Global Positioning System (GPS), will allow rapid dissemination of information on pollution levels at multiple sites simultaneously. The AQ report generated can be then published using Internet GIS to provide a real-time information service for the PCD, for increase public awareness and enhanced public participation. The local deterministic and geostatistical interpolation methods have been used for spatial prediction, and to find out the most suitable method for studying air pollution, based on observations at each monitoring site.