A Nairobi experiment in using low cost air quality monitors

Many African cities have growing air quality problems, but few have air quality monitoring systems in place. Low cost air quality sensors have the potential to bridge this data gap. This study describes the experimental deployment of six low cost air quality monitors consisting of an optical particle counter Alphasense OPC-N2 for measuring PM1, PM2.5 and PM10, and Alphasense A-series electrochemical (amperometric) gas sensors: NO2-A43F, SO2-A4, NO-A4 for measuring NO2, NO and SO2 in four schools, the United Nations Environment Program (UNEP) headquarters and a community center in Nairobi. The monitors were deployed on May 1 2016 and are still logging data. This paper analyses the data from May 1 2016 to Jan 11 2017. By examining the data produced by these sensors, we illustrate the strengths, as well as the technical limitations of using low cost sensors for monitoring air quality. We show that despite technical limitations, sensors can provide indicative measurements of air quality that are valuable to local communities. It was also found that such a sensor network can play an important role in engaging citizens by raising awareness about the importance of addressing poor air quality. We conclude that these sensors are clearly a potentially important complement but not a substitute for high quality and reliable air quality monitoring systems as problems of calibration, certification, quality control and reporting remain to be solved.

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Application of Low-Cost Air Quality Monitoring Sensor to Assess the Exposure of Ambient Air Pollution Due to PM2.5 and PM10

Lecture Notes in Civil Engineering - Sustainability in Environmental Engineering and Science ◽

10.1007/978-981-15-6887-9_16 ◽

2020 ◽

pp. 135-148

Author(s):

Md. Noman Munshi ◽

S. M. Nihab Ahsan ◽

Md. Shafinur Rahman ◽

M. Tauhid Ur Rahman

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Ambient Air ◽

Quality Monitoring ◽

Ambient Air Pollution ◽

Air Quality Monitoring ◽

Pm2.5 And Pm10

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Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development

Sensors ◽

10.3390/s18093021 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3021 ◽

Cited By ~ 26

Author(s):

Zeba Idrees ◽

Zhuo Zou ◽

Lirong Zheng

Keyword(s):

Air Quality ◽

Power Consumption ◽

Real Time ◽

Low Cost ◽

Edge Computing ◽

Quality Monitoring ◽

Quality Data ◽

Monitoring Systems ◽

Air Quality Monitoring ◽

Computing Device

With the swift growth in commerce and transportation in the modern civilization, much attention has been paid to air quality monitoring, however existing monitoring systems are unable to provide sufficient spatial and temporal resolutions of the data with cost efficient and real time solutions. In this paper we have investigated the issues, infrastructure, computational complexity, and procedures of designing and implementing real-time air quality monitoring systems. To daze the defects of the existing monitoring systems and to decrease the overall cost, this paper devised a novel approach to implement the air quality monitoring system, employing the edge-computing based Internet-of-Things (IoT). In the proposed method, sensors gather the air quality data in real time and transmit it to the edge computing device that performs necessary processing and analysis. The complete infrastructure & prototype for evaluation is developed over the Arduino board and IBM Watson IoT platform. Our model is structured in such a way that it reduces the computational burden over sensing nodes (reduced to 70%) that is battery powered and balanced it with edge computing device that has its local data base and can be powered up directly as it is deployed indoor. Algorithms were employed to avoid temporary errors in low cost sensor, and to manage cross sensitivity problems. Automatic calibration is set up to ensure the accuracy of the sensors reporting, hence achieving data accuracy around 75–80% under different circumstances. In addition, a data transmission strategy is applied to minimize the redundant network traffic and power consumption. Our model acquires a power consumption reduction up to 23% with a significant low cost. Experimental evaluations were performed under different scenarios to validate the system’s effectiveness.

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Low Cost, Multi-Pollutant Sensing System Using Raspberry Pi for Indoor Air Quality Monitoring

Sustainability ◽

10.3390/su13010370 ◽

2021 ◽

Vol 13 (1) ◽

pp. 370

Author(s):

He Zhang ◽

Ravi Srinivasan ◽

Vikram Ganesan

Keyword(s):

Air Quality ◽

Indoor Air Quality ◽

Indoor Air ◽

Low Cost ◽

Office Building ◽

Quality Monitoring ◽

Raspberry Pi ◽

Air Quality Monitoring ◽

Residential Unit ◽

Pm2.5 And Pm10

Deteriorating levels of indoor air quality is a prominent environmental issue that results in long-lasting harmful effects on human health and wellbeing. A concurrent multi-parameter monitoring approach accounting for most crucial indoor pollutants is critical and essential. The challenges faced by existing conventional equipment in measuring multiple real-time pollutant concentrations include high cost, limited deployability, and detectability of only select pollutants. The aim of this paper is to present a comprehensive indoor air quality monitoring system using a low-cost Raspberry Pi-based air quality sensor module. The custom-built system measures 10 indoor environmental conditions including pollutants: temperature, relative humidity, Particulate Matter (PM)2.5, PM10, Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Carbon dioxide (CO2), and Total Volatile Organic Compounds (TVOCs). A residential unit and an educational office building was selected and monitored over a span of seven days. The recorded mean PM2.5, and PM10 concentrations were significantly higher in the residential unit compared to the office building. The mean NO2, SO2, and TVOC concentrations were comparatively similar for both locations. Spearman rank-order analysis displayed a strong correlation between particulate matter and SO2 for both residential unit and the office building while the latter depicted strong temperature and humidity correlation with O3, SO2, PM2.5, and PM10 when compared to the former.

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On the Security and Data Integrity of Low-Cost Sensor Networks for Air Quality Monitoring

Sensors ◽

10.3390/s18124451 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4451 ◽

Cited By ~ 10

Author(s):

Lan Luo ◽

Yue Zhang ◽

Bryan Pearson ◽

Zhen Ling ◽

Haofei Yu ◽

...

Keyword(s):

Air Quality ◽

Low Cost ◽

Measurement Data ◽

Data Integrity ◽

Pollution Monitoring ◽

Quality Monitoring ◽

Monitoring Systems ◽

Air Quality Monitoring ◽

Air Pollution Monitoring ◽

The Public

The emerging connected, low-cost, and easy-to-use air quality monitoring systems have enabled a paradigm shift in the field of air pollution monitoring. These systems are increasingly being used by local government and non-profit organizations to inform the public, and to support decision making related to air quality. However, data integrity and system security are rarely considered during the design and deployment of such monitoring systems, and such ignorance leaves tremendous room for undesired and damaging cyber intrusions. The collected measurement data, if polluted, could misinform the public and mislead policy makers. In this paper, we demonstrate such issues by using a.com, a popular low-cost air quality monitoring system that provides an affordable and continuous air quality monitoring capability to broad communities. To protect the air quality monitoring network under this investigation, we denote the company of interest as a.com. Through a series of probing, we are able to identify multiple security vulnerabilities in the system, including unencrypted message communication, incompetent authentication mechanisms, and lack of data integrity verification. By exploiting these vulnerabilities, we have the ability of “impersonating” any victim sensor in the a.com system and polluting its data using fabricated data. To the best of our knowledge, this is the first security analysis of low-cost and connected air quality monitoring systems. Our results highlight the urgent need in improving the security and data integrity design in these systems.

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