Indoor and Outdoor Air Quality Detection using Programmable Microprocessor and Sensor Technologies

Most living forms of life need clean water, air and nutrient resources to maintain a healthy life. The increased population and industrial and technological development have caused more energy needs. This affects air quality (AQ) and hence human health negatively. Because AQ is critical for human health, various measurement and analysis methods are developed and the amount and variety of airborne pollutants are examined by today's methods such as passive and active samplers, automatic analyzers and remote sensors. In this study, AQ measurement is aimed to create an alternative to ready remote sensing systems by designing low cost and programmable microprocessor system which allows in place and instant data collection. Arduino, an electronic prototype platform, is used to collect, transfer and process sensor data. An interface was coded using the Visual Studio to make the data instantaneously analyzed by any program on the computer. The BEUHavaKalite device is a handheld AQ measurement device providing a wide range of measurements, gas diversity, calibrated according to the internal and external environment, high sensitivity and low cost. The other unit of this system is HavaKaliteSoft, the user interface for transferring and processing the sensor measurement results to the computer. This system tests have been carried out in Tatvan and Merkez districts of Bitlis province and the measurements confirm the accuracy of the device. The device is especially important because it allows scientists working in this field to collect data related to the field of AQ and carry out detailed studies.

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Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration

Atmospheric Measurement Techniques ◽

10.5194/amt-14-995-2021 ◽

2021 ◽

Vol 14 (2) ◽

pp. 995-1013

Author(s):

Colby Buehler ◽

Fulizi Xiong ◽

Misti Levy Zamora ◽

Kate M. Skog ◽

Joseph Kohrman-Glaser ◽

...

Keyword(s):

Air Quality ◽

Low Cost ◽

Personal Exposure ◽

Atmospheric Pollutants ◽

Sensor Data ◽

Concentration Data ◽

Spatiotemporal Resolution ◽

Online Calibration ◽

High Spatiotemporal Resolution ◽

Wide Range

Abstract. The distribution and dynamics of atmospheric pollutants are spatiotemporally heterogeneous due to variability in emissions, transport, chemistry, and deposition. To understand these processes at high spatiotemporal resolution and their implications for air quality and personal exposure, we present custom, low-cost air quality monitors that measure concentrations of contaminants relevant to human health and climate, including gases (e.g., O3, NO, NO2, CO, CO2, CH4, and SO2) and size-resolved (0.3–10 µm) particulate matter. The devices transmit sensor data and location via cellular communications and are capable of providing concentration data down to second-level temporal resolution. We produce two models: one designed for stationary (or mobile platform) operation and a wearable, portable model for directly measuring personal exposure in the breathing zone. To address persistent problems with sensor drift and environmental sensitivities (e.g., relative humidity and temperature), we present the first online calibration system designed specifically for low-cost air quality sensors to calibrate zero and span concentrations at hourly to weekly intervals. Monitors are tested and validated in a number of environments across multiple outdoor and indoor sites in New Haven, CT; Baltimore, MD; and New York City. The evaluated pollutants (O3, NO2, NO, CO, CO2, and PM2.5) performed well against reference instrumentation (e.g., r=0.66–0.98) in urban field evaluations with fast e-folding response times (≤ 1 min), making them suitable for both large-scale network deployments and smaller-scale targeted experiments at a wide range of temporal resolutions. We also provide a discussion of best practices on monitor design, construction, systematic testing, and deployment.

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Stationary and Portable Multipollutant Monitors for High Spatiotemporal Resolution Air Quality Studies including Online Calibration

10.5194/amt-2020-217 ◽

2020 ◽

Author(s):

Colby Buehler ◽

Fulizi Xiong ◽

Misti Levy Zamora ◽

Kate M. Skog ◽

Joseph Kohrman-Glaser ◽

...

Keyword(s):

Air Quality ◽

Low Cost ◽

Personal Exposure ◽

Atmospheric Pollutants ◽

Sensor Data ◽

Concentration Data ◽

Spatiotemporal Resolution ◽

Online Calibration ◽

High Spatiotemporal Resolution ◽

Wide Range

Abstract. The distribution and dynamics of atmospheric pollutants are spatiotemporally heterogeneous due to variability in emissions, transport, chemistry, and deposition. To understand these processes at high spatiotemporal resolution and their implications for air quality and personal exposure, we present custom, low-cost air quality monitors that measure concentrations of contaminants relevant to human health and climate, including gases (e.g. O3, NO, NO2, CO, CO2, CH4, and SO2) and size-resolved (0.3–10 µm) particulate matter. The devices transmit sensor data and location via cellular communications, and are capable of providing concentration data down to second-level temporal resolution. We produce two models; one designed for stationary (or mobile platform) operation, and a wearable, portable model for directly measuring personal exposure in the breathing zone. To address persistent problems with sensor drift and environmental sensitivities (e.g. relative humidity and temperature), we present the first online calibration system designed specifically for low-cost air quality sensors to calibrate zero and span concentrations at hourly to weekly intervals. Monitors are tested and validated in a number of environments across multiple outdoor and indoor sites in New Haven, CT, Baltimore, MD, and New York City. The evaluated pollutants (O3, NO2, NO, CO, CO2, and PM2.5) performed well against reference instrumentation (e.g. r = 0.66–0.98) in urban field evaluations with fast e-folding response times (≤ 1 min), making them suitable for both large-scale network deployments and smaller-scale targeted experiments at a wide range of temporal resolutions. We also provide a discussion of best practices on monitor design, construction, systematic testing, and deployment.

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Overview of Popular Techniques of Raman Spectroscopy and Their Potential in the Study of Plant Tissues

Molecules ◽

10.3390/molecules26061537 ◽

2021 ◽

Vol 26 (6) ◽

pp. 1537

Author(s):

Aneta Saletnik ◽

Bogdan Saletnik ◽

Czesław Puchalski

Keyword(s):

Raman Spectroscopy ◽

Cell Walls ◽

Structural Changes ◽

Spatial Information ◽

Technological Development ◽

Analytical Techniques ◽

High Sensitivity ◽

Plant Tissues ◽

Wide Range ◽

Identification Technique

Raman spectroscopy is one of the main analytical techniques used in optical metrology. It is a vibration, marker-free technique that provides insight into the structure and composition of tissues and cells at the molecular level. Raman spectroscopy is an outstanding material identification technique. It provides spatial information of vibrations from complex biological samples which renders it a very accurate tool for the analysis of highly complex plant tissues. Raman spectra can be used as a fingerprint tool for a very wide range of compounds. Raman spectroscopy enables all the polymers that build the cell walls of plants to be tracked simultaneously; it facilitates the analysis of both the molecular composition and the molecular structure of cell walls. Due to its high sensitivity to even minute structural changes, this method is used for comparative tests. The introduction of new and improved Raman techniques by scientists as well as the constant technological development of the apparatus has resulted in an increased importance of Raman spectroscopy in the discovery and defining of tissues and the processes taking place in them.

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Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽

10.3390/s21103338 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3338

Author(s):

Ivan Vajs ◽

Dejan Drajic ◽

Nenad Gligoric ◽

Ilija Radovanovic ◽

Ivan Popovic

Keyword(s):

Machine Learning ◽

Air Quality ◽

Relative Humidity ◽

Low Cost ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Lower Accuracy ◽

Wide Range ◽

The Impact ◽

Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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NanoSen-AQM: From Sensors to Users

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i04.11871 ◽

2020 ◽

Vol 16 (04) ◽

pp. 51

Author(s):

Pedro Lucas ◽

Jorge Silva ◽

Filipe Araujo ◽

Catarina Silva ◽

Paulo Gil ◽

...

Keyword(s):

Air Quality ◽

Low Cost ◽

Sensor Data ◽

Quality Data ◽

Easy Access ◽

The Public ◽

Nano Sensors ◽

The Cost ◽

Air Quality Data ◽

Monitoring Platform

With the raising of environmental concerns regarding pollution, interest in monitoring air quality is increasing. However, air pollution data is mostly originated from a limited number of government-owned sensors, which can only capture a small fraction of reality. Improving air quality coverage in-volves reducing the cost of sensors and making data widely available to the public. To this end, the NanoSen-AQM project proposes the usage of low-cost nano-sensors as the basis for an air quality monitoring platform, capa-ble of collecting, aggregating, processing, storing, and displaying air quality data. Being an end-to-end system, the platform allows sensor owners to manage their sensors, as well as define calibration functions, that can im-prove data reliability. The public can visualize sensor data in a map, define specific clusters (groups of sensors) as favorites and set alerts in the event of bad air quality in certain sensors. The NanoSen-AQM platform provides easy access to air quality data, with the aim of improving public health.

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Stand-alone low-cost sensor network in the inner city of Munich for modeling urban air pollutants

10.5194/egusphere-egu21-15182 ◽

2021 ◽

Author(s):

Adrian Wenzel ◽

Jia Chen ◽

Florian Dietrich ◽

Sebastian T. Thekkekara ◽

Daniel Zollitsch ◽

...

Keyword(s):

Air Quality ◽

High Resolution ◽

Sensor Network ◽

Air Pollutants ◽

Low Cost ◽

Air Pollutant ◽

Sensor Nodes ◽

Sensor Data ◽

Small Scale ◽

Urban Air

Modeling urban air pollutants is a challenging task not only due to the complicated, small-scale topography but also due to the complex chemical processes within the chemical regime of a city. Nitrogen oxides (NOx), particulate matter (PM) and other tracer gases, e.g. formaldehyde, hold information about which chemical regime is present in a city. As we are going to test and apply chemical models for urban pollution &#8211; especially with respect to spatial and temporally variability &#8211; measurement data with high spatial and temporal resolution are critical.Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O3) or carbon monoxide (CO) are large and costly, they are usually only sparsely distributed throughout a city. Hence, the official monitoring sites are not sufficient to investigate whether small-scale variability and its integrated effects are captured well by models. Smart networks consisting of small low-cost air pollutant sensors have the ability to provide the required grid density and are therefore the tool of choice when it comes to setting up or validating urban modeling frameworks. Such sensor networks have been established and run by several groups, achieving spatial and temporal high-resolution concentration maps [1, 2].After having conducted a measurement campaign in 2016 to create a high-resolution NO2 concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O3 and CO concentrations as well as meteorological parameters [4]. The sensors are stand-alone, so that they do not demand mains supply, which gives us a high flexibility in their deployment. Validating air quality models not only requires dense but also high-accuracy measurements. Therefore, we will calibrate our sensor nodes on a weekly basis using a mobile reference instrument and apply the gathered sensor data to a Machine Learning model of the sensor nodes. This will help minimize the often occurring drawbacks of low-cost sensors such as sensor drift, environmental influences and sensor cross sensitivities.&#160;[1] Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717&#8211;3735, https://doi.org/10.5194/amt-11-3717-2018, 2018[2] Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937&#8211;1946, https://doi.org/10.5194/amt-11-1937-2018, 2018[3] Zhu, Y., Chen, J., Bi, X., Kuhlmann, G., Chan, K. L., Dietrich, F., Brunner, D., Ye, S., and Wenig, M.: Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities, Atmos. Chem. Phys., 20, 13241&#8211;13251, https://doi.org/10.5194/acp-20-13241-2020, 2020[4] Zollitsch, D., Chen, J., Dietrich, F., Voggenreiter, B., Setili, L., and Wenig, M.: Low-Cost Air Quality Sensor Network in Munich, EGU General Assembly 2020, Online, 4&#8211;8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020

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GNSS-ISE: Instruction Set Extension for GNSS Baseband Processing

Sensors ◽

10.3390/s20020465 ◽

2020 ◽

Vol 20 (2) ◽

pp. 465 ◽

Cited By ~ 1

Author(s):

Krzysztof Marcinek ◽

Witold A. Pleskacz

Keyword(s):

Flash Memory ◽

Low Cost ◽

Satellite System ◽

Microprocessor System ◽

Instruction Set ◽

Advantages And Disadvantages ◽

Wide Range ◽

Instruction Set Extension ◽

Baseband Processing ◽

Global Navigation Satellite

This work presents the results of research toward designing an instruction set extension dedicated to Global Navigation Satellite System (GNSS) baseband processing. The paper describes the state-of-the-art techniques of GNSS receiver implementation. Their advantages and disadvantages are discussed. Against this background, a new versatile instruction set extension for GNSS baseband processing is presented. The authors introduce improved mechanisms for instruction set generation focused on multi-channel processing. The analytical approach used by the authors leads to the introduction of a GNSS-instruction set extension (ISE) for GNSS baseband processing. The developed GNSS-ISE is simulated extensively using PC software and field-programmable gate array (FPGA) emulation. Finally, the developed GNSS-ISE is incorporated into the first-in-the-world, according to the authors’ best knowledge, integrated, multi-frequency, and multi-constellation microcontroller with embedded flash memory. Additionally, this microcontroller may serve as an application processor, which is a unique feature. The presented results show the feasibility of implementing the GNSS-ISE into an embedded microprocessor system and its capability of performing baseband processing. The developed GNSS-ISE can be implemented in a wide range of applications including smart IoT (internet of things) devices or remote sensors, fostering the adaptation of multi-frequency and multi-constellation GNSS receivers to the low-cost consumer mass-market.

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Semantic Traffic Sensor Data: The TRAFAIR Experience

Applied Sciences ◽

10.3390/app10175882 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5882

Author(s):

Federico Desimoni ◽

Sergio Ilarri ◽

Laura Po ◽

Federica Rollo ◽

Raquel Trillo-Lado

Keyword(s):

Air Quality ◽

Real Time ◽

Smart Cities ◽

Low Cost ◽

Semantic Annotation ◽

Open Data ◽

Transportation Systems ◽

Sensor Data ◽

Time Data ◽

Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

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Dynamic Frame Update Policy for UHF RFID Sensor Tag Collisions

Sensors ◽

10.3390/s20092696 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2696 ◽

Cited By ~ 1

Author(s):

Laura Arjona ◽

Hugo Landaluce ◽

Asier Perallos ◽

Enrique Onieva

Keyword(s):

Radio Frequency Identification ◽

Low Cost ◽

Object Identification ◽

Sensor Data ◽

Uhf Rfid ◽

Slotted Aloha ◽

Frame Size ◽

Passive Uhf Rfid ◽

Wide Range ◽

Class 1

The current growing demand for low-cost edge devices to bridge the physical–digital divide has triggered the growing scope of Radio Frequency Identification (RFID) technology research. Besides object identification, researchers have also examined the possibility of using RFID tags for low-power wireless sensing, localisation and activity inference. This paper focuses on passive UHF RFID sensing. An RFID system consists of a reader and various numbers of tags, which can incorporate different kinds of sensors. These sensor tags require fast anti-collision protocols to minimise the number of collisions with the other tags sharing the reader’s interrogation zone. Therefore, RFID application developers must be mindful of anti-collision protocols. Dynamic Frame Slotted Aloha (DFSA) anti-collision protocols have been used extensively in the literature because EPCglobal Class 1 Generation 2 (EPC C1G2), which is the current communication protocol standard in RFID, employs this strategy. Protocols under this category are distinguished by their policy for updating the transmission frame size. This paper analyses the frame size update policy of DFSA strategies to survey and classify the main state-of-the-art of DFSA protocols according to their policy. Consequently, this paper proposes a novel policy to lower the time to read one sensor data packet compared to existing strategies. Next, the novel anti-collision protocol Fuzzy Frame Slotted Aloha (FFSA) is presented, which applies this novel DFSA policy. The results of our simulation confirm that FFSA significantly decreases the sensor tag read time for a wide range of tag populations when compared to earlier DFSA protocols thanks to the proposed frame size update policy.

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Gas Sensing Properties of TiO2 and SnO2 Nanopowders Obtained through Gel Combustion

Advances in Science and Technology ◽

10.4028/www.scientific.net/ast.45.1828 ◽

2006 ◽

Vol 45 ◽

pp. 1828-1833

Author(s):

Fabio A. Deorsola ◽

P. Mossino ◽

Ignazio Amato ◽

Bruno DeBenedetti ◽

A. Bonavita ◽

...

Keyword(s):

Response Time ◽

Metal Oxides ◽

Gas Sensing ◽

Low Cost ◽

High Sensitivity ◽

Fast Response ◽

High Specific Surface Area ◽

Research Field ◽

Wide Range ◽

Gel Combustion

Nanostructured semiconductor metal oxides have played a central role in the gas sensing research field, because of their high sensitivity, selectivity and low response time. Among all the processes, developed for the synthesis of nanostructured metal oxides, gel combustion seems to be the most promising route due to low-cost precursors and simplicity of the process. It combines chemical gelation and combustion, involving the formation of a gel from an acqueous solution and an exothermic redox reaction, yielding to very porous and softly agglomerated nanopowders. In this work, nanostructured tin oxide, SnO2, and titanium oxide, TiO2, have been synthesized through gel combustion. Powders showed nanometric particle size and high specific surface area. The so-obtained TiO2 and SnO2 nanopowders have been used as sensitive element of resistive λ sensor and ethanol sensor respectively, realized depositing films of nanopowders dispersed in water onto alumina substrates provided with Pt contacts and heater. TiO2-based sensors showed at high temperature good response, fast response time, linearity in a wide range of O2 concentration and long-term stability. SnO2-based sensors have shown high sensitivity to low concentrations of ethanol at moderate temperature.

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