scholarly journals The use of low-cost measuring devices for testing air quality in hard-to-reach locations

2018 ◽  
Vol 44 ◽  
pp. 00151
Author(s):  
Mariusz Rogulski
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Low Cost ◽  
Portable Devices ◽  
Tethered Balloon ◽  
Free Flight ◽  
Hot Air ◽  
Measuring Devices ◽  
Air Quality Assessment ◽  
Ground Monitoring

Air quality assessment is traditionally carried out by ground monitoring. With the development of technology and the creation of small, low-cost sensors, it became possible to effectively study lower tropospheric layers by using light aircraft and balloons. The article presents the use of designed small, portable devices using low-cost dust sensors to research air pollutants with using a hot air balloon. The results of measurements of PM10 concentration using tethered balloon flights and during free flight are presented.

Air quality assessment using the Pollen Abortion assay in Tradescantia pallida in a Mid-sized City in Southern Brazil

2021 ◽  
Vol 26 (1) ◽  
pp. 6-16
Author(s):  
Leonardo de Vasconcellos Ceglinski ◽  
Edariane Menestrino Garcia ◽  
Fernanda Oliveira Reis ◽  
Ronan Adler Tavella ◽  
Flavio Manoel Rodrigues da Silva Júnior
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Environmental Changes ◽  
Low Cost ◽  
Control Point ◽  
Pollen Abortion ◽  
Air Quality Assessment ◽  
Tradescantia Pallida ◽  
Air Quality Monitoring Stations ◽  
Sessile Organisms

Urbanization is an important source of air pollutants that can compromise human health. In developing countries, such as Brazil, most cities do not have air quality monitoring stations. Assessing air quality through plant species has gained recognized prominence, as they are sessile organisms and sensitive to environmental changes. Pollen abortion assay in Tradescantia pallida is a fast and low-cost bioassay that can be implemented in passive biomonitoring scenarios. The present study aimed to use the pollen abortion assay in T. pallida to assess air quality in the municipality of Rio Grande, RS, Brazil and the possible relationship with vehicular flow. A relation was found between the highest rate of pollen abortion and the sites where there was greater vehicular flow and ozone levels, while at the control point, the lowest rate of pollen abortion among the others was found, corroborating the hypothesis that air pollution together with high levels of ozone from vehicles, impair plant pollination.

scholarly journals Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: Impacts of COVID-19 pandemic lockdown

2020 ◽  
Author(s):  
Shibao Wang ◽  
Yun Ma ◽  
Zhongrui Wang ◽  
Lei Wang ◽  
Xuguang Chi ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Low Cost ◽  
Air Pollutant ◽  
Mitigation Strategies ◽  
Traffic Emissions ◽  
Urban Air ◽  
Industrial Emissions ◽  
Gis Technology ◽  
Mobile Monitoring

Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyper-local scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (Oct. 2019–Sep. 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspots identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. While the O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutants levels in urban regions. This research demonstrates the sense power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at urban micro-scale.

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

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

<p>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 – especially with respect to spatial and temporally variability – measurement data with high spatial and temporal resolution are critical.</p><p>Since governmental monitoring stations of air pollutants such as PM, NOx, ozone (O<sub>3</sub>) 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].</p><p>After having conducted a measurement campaign in 2016 to create a high-resolution NO<sub>2</sub> concentration map for Munich [3], we are currently setting up a low-cost sensor network to measure NOx, PM, O<sub>3</sub> 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.</p><p> </p><p>[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, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018</p><p>[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, 1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018</p><p>[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, 13241–13251, https://doi.org/10.5194/acp-20-13241-2020, 2020</p><p>[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, 4–8 May 2020, EGU2020-19276, https://doi.org/10.5194/egusphere-egu2020-19276, 2020</p>

Low-Cost Air Quality Sensor Network in Munich

2020 ◽  
Author(s):  
Daniel Zollitsch ◽  
Jia Chen ◽  
Florian Dietrich ◽  
Benno Voggenreiter ◽  
Luca Setili ◽  
...  
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Air Pollutants ◽  
Low Cost ◽  
Environmental Parameters ◽  
Calibration Method ◽  
Sensitivity Function ◽  
Machine Learning Algorithms ◽  
Urban Air ◽  
Monitoring Networks

<p>As the number of official monitoring stations for measuring urban air pollutants such as nitrogen oxides (NOx), particulate matter (PM) or ozone (O<sub>3</sub>) in most cities is quite small, it is difficult to determine the real human exposure to those pollutants. Therefore, several groups have established spatially higher resolved monitoring networks using low-cost sensors to create a finer concentration map [1-3].</p><p>We are currently establishing a low-cost, but high-accuracy network in Munich to measure the concentrations of NOx, PM, O<sub>3</sub>, CO and additional environmental parameters. For that, we developed a compact stand-alone sensor systems that requires low power, automatically measures the respective parameters every minute and sends the data to our server. There the raw data is transferred into concentration values by applying the respective sensitivity function for each sensor. These functions are determined by calibration measurements prior to the distribution of the sensors.</p><p>In contrast to the other existing networks, we will apply a recurring calibration method using a mobile high precision calibration unit (reference sensor) and machine learning algorithms. The results will be used to update the sensitivity function of each single sensor twice a week.  With the help of this approach, we will be able to create a calibrated real-time concentration map of air pollutants in Munich.</p><p>[1] Bigi et al.: Performance of NO, NO<sub>2</sub> low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, 2018</p><p>[2] Popoola et al., “Use of networks of low cost air quality sensors to quantify air quality in urban settings,” Atmos. Environ., 194, 58–70, 2018</p><p>[3] Schneider et al.: Mapping urban air quality in near real-time using observations from low-cost sensors and model information, Environ. Int., 106, 234–247, 2017</p>

scholarly journals Application of Low-Cost Fine Particulate Mass Monitors to Convert Satellite Aerosol Optical Depth Measurements to Surface Concentrations in North America and Africa

2020 ◽  
Author(s):  
Carl Malings ◽  
Daniel Westervelt ◽  
Aliaksei Hauryliuk ◽  
Albert A. Presto ◽  
Andrew Grieshop ◽  
...  
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Satellite Data ◽  
Low Cost ◽  
Sub Saharan Africa ◽  
Quality Data ◽  
Data Coverage ◽  
Particulate Mass ◽  
Ground Monitoring

Abstract. Low-cost particulate mass sensors provide opportunities to assess air quality at unprecedented spatial and temporal resolutions. Established traditional monitoring networks have limited spatial resolution and are frequently absent in less-developed countries (e.g. in sub-Saharan Africa). Satellites provide snapshots of regional air pollution, but require ground-truthing. Low-cost monitors can supplement and extend data coverage from these sources worldwide, providing a better overall air quality picture. We demonstrate such a multi-source data integration using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are present, and are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system. We assess the performance of linear conversion factors for AOD to surface PM2.5 using both networks, and identify relative benefits provided by the denser low-cost sensor network. In particular, with 10 or more ground monitors in the city, there is a two-fold reduction in worst-case surface PM2.5 estimation mean absolute error compared to using only a single ground monitor. Second, in Rwanda, Malawi, and the Democratic Republic of the Congo, traditional ground-based monitoring is lacking and must be substituted with low-cost sensor data. Here, we assess the ability of regional-scale satellite retrievals and local-scale low-cost sensor measurements to complement each other. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement (in terms of surface PM2.5 estimation accuracy) with respect to using ground monitoring data alone. Overall, we find that combining ground-based low-cost sensor and satellite data can improve and expand spatio-temporal air quality data coverage in both well-monitored and data-sparse regions.

Air Quality Assessment by Neural Networks

2011 ◽  
pp. 91-117
Author(s):  
Petr Hájek ◽  
Vladimír Olej
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Air Quality ◽  
Quality Assessment ◽  
Air Pollutants ◽  
Quality Indices ◽  
Feature Maps ◽  
Local Conditions ◽  
Mean Values ◽  
Air Quality Assessment

The chapter presents an overview of current methods for air quality assessment, i.e. air stress indices and air quality indices. Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected air pollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g. using systems based on neural networks and fuzzy logic. Therefore, the chapter presents a design of a model for air quality assessment based on a combination of Kohonen’s self-organizing feature maps and fuzzy logic neural networks. The model makes it possible to analyze the structure of data, to find localities with similar air quality, and to interpret the classification results by means of fuzzy logic. Due to its generalization ability, it is also possible to classify unknown localities into classes assessing their air quality.

Networks of Air Quality Sensors and Their Use for High-resolution Mapping of Urban Air Quality

2020 ◽  
Author(s):  
Philipp Schneider ◽  
Nuria Castell ◽  
Paul Hamer ◽  
Sam-Erik Walker ◽  
Alena Bartonova
Keyword(s):  
Sensor Networks ◽  
Air Quality ◽  
High Resolution ◽  
Air Pollutants ◽  
Low Cost ◽  
Urban Air Quality ◽  
Urban Air ◽  
Sensor Systems ◽  
Air Quality Model ◽  
Quality Model

<p>One of the most promising applications of low-cost sensor systems for air quality is the possibility to deploy them in relatively dense networks and to use this information for mapping urban air quality at unprecedented spatial detail. More and more such dense sensor networks are being set up worldwide, particularly for relatively inexpensive nephelometers that provide PM<sub>2.5</sub> observations with often quite reasonable accuracy. However, air pollutants typically exhibit significant spatial variability in urban areas, so using data from sensor networks alone tends to result in maps with unrealistic spatial patterns, unless the network density is extremely high. One solution is to use the output from an air quality model as an a priori field and as such to use the combined knowledge of both model and sensor network to provide improved maps of urban air quality. Here we present our latest work on combining the observations from low-cost sensor systems with data from urban-scale air quality models, with the goal of providing realistic, high-resolution, and up-to-date maps of urban air quality.</p><p>In previous years we have used a geostatistical approach for mapping air quality (Schneider et al., 2017), exploiting both low-cost sensors and model information. The system has now been upgraded to a data assimilation approach that integrates the observations from a heterogeneous sensor network into an urban-scale air quality model while considering the sensor-specific uncertainties. The approach further ensures that the spatial representativity of each observation is automatically derived as a combination of a model climatology and a function of distance. We demonstrate the methodology using examples from Oslo and other cities in Norway. Initial results indicate that the method is robust and provides realistic spatial patterns of air quality for the main air pollutants that were evaluated, even in areas where only limited observations are available. Conversely, the model output is constrained by the sensor data, thus adding value to both input datasets.</p><p>While several challenging issues remain, modern air quality sensor systems have reached a maturity level at which some of them can provide an intra-sensor consistency and robustness that makes it feasible to use networks of such systems as a data source for mapping urban air quality at high spatial resolution. We present our current approach for mapping urban air quality with the help of low-cost sensor networks and demonstrate both that it can provide realistic results and that the uncertainty of each individual sensor system can be taken into account in a robust and meaningful manner.</p><p> </p><p>Schneider, P., Castell N., Vogt M., Dauge F. R., Lahoz W. A., and Bartonova A., 2017. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment international, 106, 234-247.</p>

scholarly journals Urban Air Quality: Assessing Traffiffic and Building Architecture Impacts using Portable Measuring Devices in Toronto, Ontario

2019 ◽  
Vol 5 (1) ◽  
pp. 5-12
Author(s):  
Cuilian Fang ◽  
Cheol H. Jeong ◽  
Greg J. Evans
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Ultrafine Particles ◽  
Tall Buildings ◽  
Urban Pollution ◽  
Urban Air Quality ◽  
Urban Air ◽  
Portable Devices ◽  
Pollution Levels ◽  
King Street

Vehicle emissions are one of the largest local contributors to poor urban air quality. High emissions are often associated with traffic congestion, and pollution may also become trapped between tall buildings creating a street canyon effect. The spatial variability of traffic-related air pollutants in microenvironments should be considered in evaluating changes in urban planning. This study focuses on assessing the air quality and commuter exposure in Toronto, Ontario, Canada, specifically focusing on the effect of the King Street Pilot Project on local urban air quality by reducing traffic. Increased vehicular density is expected to contribute to higher urban pollution levels and tall buildings may trap these contaminants. Field measurements were made within the King Street Pilot area during weekday rush hours to capture the best representation of peak activity and pollutant levels when there were similar average wind speeds and directions for the sampling dates. A suite of portable devices was carried along predesigned and timed routes through traffic dense areas to measure vehicle-related air pollutants including black carbon (BC), ultrafine particles (UFP, particles smaller than 0.1 μm), and particulate matter (PM2.5, particles smaller than 2.5 μm). Data was normalized, corrected and analyzed using centralized pollutant while considering meteorological site measurements located about 1.5 km away from the study area. Results indicated higher BC and UFP levels during peak commuting times between 8 am to 10 am and relatively increased pollution levels within the area of tall buildings versus the area with shorter buildings. Strong spatial variations of BC and UFP were found, while PM2.5 levels remained relatively constant in the downtown area. Elevated levels of BC and UFP were observed around nearby construction sites. This study contributes to establishing a baseline to evaluate the King Street Pilot Project’s air quality impact as well as proposing potential methods of detailed data collection within microenvironments to observe the air quality of urban centres.

Assessment of Temporal and Spatial Trends of Air Pollutants Using Low-Cost Air Quality Sensors in Peñuelas, Puerto Rico

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Stephen Reece ◽  
Ron Williams ◽  
Maribel Colón ◽  
Evelyn Huertas ◽  
Marie O'Shea ◽  
...  
Keyword(s):  
Puerto Rico ◽  
Air Quality ◽  
Air Pollutants ◽  
Low Cost ◽  
Spatial Trends ◽  
Temporal And Spatial ◽  
Temporal And Spatial Trends

scholarly journals GeoAir—A Novel Portable, GPS-Enabled, Low-Cost Air-Pollution Sensor: Design Strategies to Facilitate Citizen Science Research and Geospatial Assessments of Personal Exposure

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3761
Author(s):  
Yoo Min Park ◽  
Sinan Sousan ◽  
Dillon Streuber ◽  
Kai Zhao
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Rapid Evolution ◽  
Personal Exposure ◽  
Quality Data ◽  
Portable Devices ◽  
Design Strategies ◽  
Laboratory Test Results ◽  
Voc Sensor

The rapid evolution of air sensor technologies has offered enormous opportunities for community-engaged research by enabling citizens to monitor the air quality at any time and location. However, many low-cost portable sensors do not provide sufficient accuracy or are designed only for technically capable individuals by requiring pairing with smartphone applications or other devices to view/store air quality data and collect location data. This paper describes important design considerations for portable devices to ensure effective citizen engagement and reliable data collection for the geospatial analysis of personal exposure. It proposes a new, standalone, portable air monitor, GeoAir, which integrates a particulate matter (PM) sensor, volatile organic compound (VOC) sensor, humidity and temperature sensor, LTE-M and GPS module, Wi-Fi, long-lasting battery, and display screen. The preliminary laboratory test results demonstrate that the PM sensor shows strong performance when compared to a reference instrument. The VOC sensor presents reasonable accuracy, while further assessments with other types of VOC are needed. The field deployment and geo-visualization of the field data illustrate that GeoAir collects fine-grained, georeferenced air pollution data. GeoAir can be used by all citizens regardless of their technical proficiency and is widely applicable in many fields, including environmental justice and health disparity research.

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