Lidar system for air-pollution monitoring over urban areas

1997 ◽  
Author(s):  
Irina V. Moskalenko ◽  
Djolinard A. Shecheglov ◽  
Nikolai A. Molodtsov
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Pollution Monitoring ◽  
Lidar System ◽  
Air Pollution Monitoring

scholarly journals Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Oscar Alvear ◽  
Nicola Roberto Zema ◽  
Enrico Natalizio ◽  
Carlos T. Calafate
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Pollution Monitoring ◽  
Mobility Models ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Air Pollution Monitoring ◽  
Acceptable Alternative ◽  
Rural Environments ◽  
Time Bounds

Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants’ concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time.

scholarly journals Hardware Efficient Solutions for Wireless Air Pollution Sensors Dedicated to Dense Urban Areas

2020 ◽  
Vol 12 (5) ◽  
pp. 776
Author(s):  
Marzena Banach ◽  
Rafał Długosz ◽  
Jolanta Pauk ◽  
Tomasz Talaśka
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Smart Cities ◽  
Real Data ◽  
High Amplitude ◽  
Base Station ◽  
Pollution Monitoring ◽  
Process Data ◽  
Signal Processing Algorithm ◽  
Air Pollution Monitoring

This paper proposes novel solutions for the application of air pollution monitoring systems in so called ‘smart cities’. A possibility of the implementation of a relatively dense network of wireless air pollution sensors that can collect and process data in real time was the motive behind our research and investigations. We discuss the concept of the wireless sensor network, taking into account the structure of the urban development in cities and we present a novel signal processing algorithm that may be used to control the communication scheme between particular sensors and an external network. We placed a special emphasis on the computational complexity to facilitate the implementation directly at the transistor level of particular sensors. The algorithm was verified using real data obtained from air pollution sensors installed in Krakow, Poland. To ensure sufficient robustness of the variability of input data, we artificially added high amplitude noise to the real data we obtained. This paper demonstrates the performance of the algorithm. This algorithm allows for the reduction of the noise amplitude by 23 dB and enables a reduction of the number of wireless communication sessions with a base station (BS) by 70%–80%. We also present selected measurement results of a prototype current-mode digital-to-analogue converter to be used in the sensors, for signal resolutions up to 7 bits.

scholarly journals Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks

2020 ◽  
Vol 12 (24) ◽  
pp. 4142
Author(s):  
Jovan Kalajdjieski ◽  
Eftim Zdravevski ◽  
Roberto Corizzo ◽  
Petre Lameski ◽  
Slobodan Kalajdziski ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Data Augmentation ◽  
Class Imbalance ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Weather Data ◽  
Generative Adversarial Networks ◽  
Class Imbalance Problem ◽  
Air Pollution Monitoring

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem.

scholarly journals Spatiotemporal Characteristics of Particulate Matters Concentration and Their Influence on Ambient Air Quality in Urban Regions of Khuzestan Province, Iran

2020 ◽  
Vol 9 (3) ◽  
pp. 209-226
Author(s):  
Nassrin Hasanzadeh ◽  
◽  
Fariba Hedayatzadeh ◽  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Ambient Air ◽  
Temporal Variations ◽  
Pollution Monitoring ◽  
Spatial And Temporal Patterns ◽  
Khuzestan Province ◽  
Air Pollution Monitoring ◽  
Pm10 And Pm2.5 ◽  
Pm2.5 And Pm10

Background: One of the most concerning pollutants in urban areas across the globe is particulate matter suspended in the Earth’s atmosphere. The main objective of the current investigation is to explore the spatial and temporal patterns of ambient air particles (PM10 and PM2.5) and PM2.5/PM10 ratio in different urban areas of Khuzestan Province. Methods: In this way, the required data were gathered from the environmental protection organization based on hourly mean concentrations of PM10 and PM2.5 of six air pollution-monitoring sites for 5 years. Results: Results indicated that the average concentrations of PM10, PM2.5, and PM2.5/PM10 are about 134.14±39.23 µg/m3, 44.51±13.44 µg/m3 and 0.33±0.07, respectively. The examinations revealed a reductive trend on annual values of PMs in terms of temporal variations. A detailed investigation of the annual mean concentrations of PMs and PM2.5/PM10 in terms of spatial variations demonstrated the largest values for Naderi-Ahvaz and Abadan stations. Furthermore, the measured AQI was larger than 100 and the Exceedance Factor (EF) values of PM10 and PM2.5 ranged between 1.51-2.73 and 0.77-1.41. The statistical analysis obtained from linear regression revealed a significant positive relation between AQI and PM2.5 and PM10 with correlation coefficients (R2) of 0.8259 and 0.7934, respectively. Conclusion: Although the analysis and measurement revealed a reductive trend in the annual mean concentrations of PM2.5 and PM10, the measured AQI and EF values are still far from the standards of good quality and low pollution. Therefore, it is highly necessary to follow the air pollution protocols to control PM air pollution in Khuzestan Province.

Air Pollution Monitoring Strategies and Technologies for Urban Areas

2013 ◽  
pp. 277-296 ◽  
Author(s):  
Thomas A. J. Kuhlbusch ◽  
Ulrich Quass ◽  
Gary Fuller ◽  
Mar Viana ◽  
Xavier Querol ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Monitoring Strategies

scholarly journals On Air Pollution Monitoring Systems using IoT

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1861-1862
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Heart Diseases ◽  
Pollution Monitoring ◽  
Monitoring Systems ◽  
Health Issues ◽  
Air Pollution Monitoring ◽  
The People

In the present day scenario, pollution caused by humans affects all the parameters of the environment (air, water, soil and sound). Air pollution is becoming a major concern in urban areas can expose the people to health issues like asthma attacks, lung problems, heart diseases etc. In this paper, two different methods are studied in monitoring the air quality of an environment. The first method involves usage of AWS cloud with a microcontroller and sensor. The second method uses Arduino along with Wi- Fi module and a sensor for measuring the level of pollutants and monitoring the air quality of a given environment.

scholarly journals MoreAir: A Low-Cost Urban Air Pollution Monitoring System

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 998 ◽  
Author(s):  
Ihsane Gryech ◽  
Yassine Ben-Aboud ◽  
Bassma Guermah ◽  
Nada Sbihi ◽  
Mounir Ghogho ◽  
...  
Keyword(s):  
Air Pollution ◽  
Monitoring System ◽  
Urban Areas ◽  
Low Cost ◽  
Urban Air Pollution ◽  
Pollution Monitoring ◽  
Geographical Information ◽  
Urban Air ◽  
Air Pollution Monitoring ◽  
Deployment Strategy

MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution.

Compact mobile lidar system based on the LabVIEW code: applications in urban air pollution monitoring in Athens, Greece

1999 ◽  
Author(s):  
Alexandros D. Papayannis ◽  
Giorgos Tsaknakis ◽  
Giorgos Chourdakis ◽  
Alexander A. Serafetinides
Keyword(s):  
Air Pollution ◽  
Urban Air Pollution ◽  
Pollution Monitoring ◽  
Urban Air ◽  
Lidar System ◽  
Air Pollution Monitoring
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