scholarly journals Establishing a Community Air Monitoring Network in a Wildfire Smoke-Prone Rural Community: The Motivations, Experiences, Challenges, and Ideas of Clean Air Methow’s Clean Air Ambassadors

2020 ◽  
Vol 17 (22) ◽  
pp. 8393
Author(s):  
Amanda Durkin ◽  
Rico Gonzalez ◽  
Tania Busch Isaksen ◽  
Elizabeth Walker ◽  
Nicole A. Errett
Keyword(s):  
Air Quality ◽  
Environmental Science ◽  
Low Cost ◽  
Geographic Location ◽  
Monitoring Network ◽  
Air Monitoring ◽  
Wildfire Smoke ◽  
Clean Air ◽  
Quality Issues ◽  
Future Project

In response to wildfire-related air quality issues as well as those associated with winter wood stove use and prescribed and agricultural burning, Clean Air Methow’s Clean Air Ambassador program established a community air monitoring network (CAMN) to provide geospatially specific air quality information and supplement data generated by the two Washington State Department of Ecology nephelometers situated in the area. Clean Air Ambassadors (CAAs) were purposefully selected to host low-cost air sensors based on their geographic location and interest in air quality. All 18 CAAs were interviewed to understand their motivations for participation, experiences using the data, challenges encountered, and recommendations for future project directions. Interview transcripts were coded, and a qualitative analysis approach was used to identify the key themes in each domain. The reported motivations for participation as a CAA included reducing personal exposure, protecting sensitive populations, interest in air quality or environmental science, and providing community benefits. CAAs used CAMN data to understand air quality conditions, minimize personal or familial exposure, and engage other community members in air quality discussions. Opportunities for future project directions included use for monitoring other seasonal air quality issues, informing or reducing other pollution-generating activities, school and community educational activities, opportunities for use by and engagement of different stakeholder groups, and mobile-friendly access to CAMN information. Limited challenges associated with participation were reported. Additional research is necessary to understand the community-level impacts of the CAMN. The findings may be informative for other rural wildfire smoke-prone communities establishing similar CAMNs.

CAMS-Net: The Clean Air Monitoring and Solutions Network

2021 ◽  
Author(s):  
Daniel Westervelt ◽  
Celeste McFarlane ◽  
Faye McNeill ◽  
R (Subu) Subramanian ◽  
Mike Giordano ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Low Cost ◽  
Air Monitoring ◽  
Quality Data ◽  
Calibration Methods ◽  
The World ◽  
Clean Air

<p>There is a severe lack of air pollution data around the world. This includes large portions of low- and middle-income countries (LMICs), as well as rural areas of wealthier nations as monitors tend to be located in large metropolises. Low cost sensors (LCS) for measuring air pollution and identifying sources offer a possible path forward to remedy the lack of data, though significant knowledge gaps and caveats remain regarding the accurate application and interpretation of such devices.</p><p>The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and best practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together at least 32 multidisciplinary member networks from North America, Europe, Africa, and India. The project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including domestic and international graduate students and postdoctoral researchers. </p><p>Here we present some preliminary research accelerated through the CAMS-Net project. Specifically, we present LCS calibration methodology for several co-locations in LMICs (Accra, Ghana; Kampala, Uganda; Nairobi, Kenya; Addis Ababa, Ethiopia; and Kolkata, India), in which reference BAM-1020 PM2.5 monitors were placed side-by-side with LCS. We demonstrate that both simple multiple linear regression calibration methods for bias-correcting LCS and more complex machine learning methods can reduce bias in LCS to close to zero, while increasing correlation. For example, in Kampala, Raw PurpleAir PM2.5 data are strongly correlated with the BAM-1020 PM2.5 (r<sup>2</sup> = 0.88), but have a mean bias of approximately 12 μg m<sup>-3</sup>. Two calibration models, multiple linear regression and a random forest approach, decrease mean bias from 12 μg m<sup>-3 </sup>to -1.84 µg m<sup>-3</sup> or less and improve the the r<sup>2</sup> from 0.88 to 0.96. We find similar performance in several other regions of the world. Location-specific calibration of low-cost sensors is necessary in order to obtain useful data, since sensor performance is closely tied to environmental conditions such as relative humidity. This work is a first step towards developing a database of region-specific correction factors for low cost sensors, which are exploding in popularity globally and have the potential to close the air pollution data gap especially in resource-limited countries. </p><p> </p><p> </p>

scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

scholarly journals The TOMPs ambient air monitoring network – Continuous data on UK air quality for over 20 years

2016 ◽  
Vol 217 ◽  
pp. 42-51 ◽  
Author(s):  
Carola Graf ◽  
Athanasios Katsoyiannis ◽  
Kevin C. Jones ◽  
Andrew J. Sweetman
Keyword(s):  
Air Quality ◽  
Monitoring Network ◽  
Ambient Air ◽  
Air Monitoring ◽  
Continuous Data ◽  
Ambient Air Monitoring

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

scholarly journals REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY

2016 ◽  
Vol XLI-B2 ◽  
pp. 729-733 ◽  
Author(s):  
L. Marek ◽  
M. Campbell ◽  
M. Epton ◽  
M. Storer ◽  
S. Kingham
Keyword(s):  
Quality Of Life ◽  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Low Cost ◽  
Monitoring Network ◽  
Environmental Data ◽  
Chronic Obstructive ◽  
Actual Usage

The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.

scholarly journals Low-Cost Air Quality Sensors: One-Year Field Comparative Measurement of Different Gas Sensors and Particle Counters with Reference Monitors at Tušimice Observatory

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 492 ◽  
Author(s):  
Petra Bauerová ◽  
Adriana Šindelářová ◽  
Štěpán Rychlík ◽  
Zbyněk Novák ◽  
Josef Keder
Keyword(s):  
Air Quality ◽  
Gas Sensors ◽  
Low Cost ◽  
Monitoring Network ◽  
Field Testing ◽  
Ambient Air ◽  
Monitoring Networks ◽  
Comparative Measurement ◽  
One Year ◽  
Pm2.5 And Pm10

With attention increasing regarding the level of air pollution in different metropolitan and industrial areas worldwide, interest in expanding the monitoring networks by low-cost air quality sensors is also increasing. Although the role of these small and affordable sensors is rather supplementary, determination of the measurement uncertainty is one of the main questions of their applicability because there is no certificate for quality assurance of these non-reference technologies. This paper presents the results of almost one-year field testing measurements, when the data from different low-cost sensors (for SO2, NO2, O3, and CO: Cairclip, Envea, FR; for PM1, PM2.5, and PM10: PMS7003, Plantower, CHN, and OPC-N2, Alphasense, UK) were compared with co-located reference monitors used within the Czech national ambient air quality monitoring network. The results showed that in addition to the given reduced measurement accuracy of the sensors, the data quality depends on the early detection of defective units and changes caused by the effect of meteorological conditions (effect of air temperature and humidity on gas sensors and effect of air humidity with condensation conditions on particle counters), or by the interference of different pollutants (especially in gas sensors). Comparative measurement is necessary prior to each sensor’s field applications.

scholarly journals Air quality in the eastern United States and Eastern Canada for 1990–2015: 25 years of change in response to emission reductions of SO<sub>2</sub> and NO<sub>x</sub> in the region

2019 ◽  
Author(s):  
Jian Feng ◽  
Elton Chan ◽  
Robert Vet
Keyword(s):  
Air Quality ◽  
Warm Season ◽  
Monitoring Network ◽  
Cold Season ◽  
Eastern United States ◽  
Eastern Canada ◽  
Spatial Changes ◽  
The Us ◽  
Clean Air ◽  
Uniform Reduction

Abstract. SO2 and NOx are precursors to form sulfate, nitrate and ammonium particles, which account for more than 50 % of PM2.5 mass in the eastern US and Eastern Canada, and are dominant components of PM2.5 during many smog events. H2SO4 and HNO3, formed from oxidation of SO2 and NOx respectively, are the main sources of acid deposition through wet and dry depositions. NOx is also a precursor to the formation of tropospheric O3, which is an important atmospheric oxidant and is also essential for the formation of other atmospheric oxidants, such as OH and H2O2. In the past 26 years from 1990 to 2015, emissions of SO2 and NOx in US were significantly reduced from 23.1 and 25.2 million tons/year in 1990 to 3.7 and 11.5 million tons/year in 2015 respectively. In Canada, SO2 and NOx were reduced by 63 % and 33 % from 1990 to 2014. In response to the significant reduction of SO2 and NOx emissions, air quality in the eastern US and Eastern Canada improved tremendously during 1990–2015. In this study, we analyzed surface air concentrations of SO42−, NO3−, NH4+, HNO3 and SO2 measured weekly by the Clean Air Status and Trends Network (CASTNET) in the US and measured daily from the Canadian Air and Precipitation Monitoring Network (CAPMoN) in Canada to reveal the temporal and spatial changes of each species during the 25-year period. For the whole the eastern US and Eastern Canada, the annual mean concentrations of SO42−, NO3−, NH4+, HNO3, SO2 and TNO3 (NO3− + HNO3, expressed as the mass of equivalent NO3−) were reduced by 73.3 %, 29.1 %, 67.4 %, 65.8 %, 87.6 % and 52.6 % respectively from 1990 to 2015. In terms of percentage, reduction of all species except NO3− was spatially uniform; reduction of SO2 and HNO3 was similar in warm season (May–October) and cold season (November–April), and reduction of SO42−, NO3− and NH4+ was more significant in warm season than in cold season. Reduction of SO42− and SO2 mainly occurred in 1989–1995 and 2007–2015 during warm season, and in 1989–1995 and 2005–2015 during cold season. Reduction of NO3− mainly occurred in the Midwest after 2000. Other than in the Midwest, NO3− had very little change during cold season for the period. The reduction of NH4+ generally followed the reduction trend of SO42−, especially after 2000 the temporal trend of NH4+ was almost identical to that of SO42−. The ratio of S in SO42− to total S in SO42− and SO2, as well as the ratio of NO3− to TNO3 increased by more than 50 % during the period. This indicates that much more percentage of SO2 was oxidized to SO42−, and much more percentage of HNO3 was neutralized to NH4NO3 in the region near the end of the period.

Developing a Community-Engaged Low-Cost Air Monitoring Network in Seattle, Washington

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Nancy Carmona ◽  
Roxanne Garcia ◽  
Natalia Kowalchuk ◽  
Edmund Seto ◽  
Lianne Sheppard ◽  
...  
Keyword(s):  
Low Cost ◽  
Monitoring Network ◽  
Air Monitoring

scholarly journals Creating Clean Air Spaces During Wildland Fire Smoke Episodes: Web Summit Summary

2021 ◽  
Vol 9 ◽  
Author(s):  
Gilliane Davison ◽  
Karoline K. Barkjohn ◽  
Gayle S. W. Hagler ◽  
Amara L. Holder ◽  
Sarah Coefield ◽  
...  
Keyword(s):  
Public Health ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Wildland Fire ◽  
Air Filtration ◽  
Wildfire Smoke ◽  
Fire Smoke ◽  
Clean Air ◽  
Pollution Events

Effective strategies to reduce indoor air pollutant concentrations during wildfire smoke events are critically needed. Worldwide, communities in areas prone to wildfires may suffer from annual smoke exposure events lasting from days to weeks. In addition, there are many areas of the world where high pollution events are common and where methods employed to reduce exposure to pollution may have relevance to wildfire smoke pollution episodes and vice versa. This article summarizes a recent virtual meeting held by the United States Environmental Protection Agency (EPA) to share research, experiences, and other information that can inform best practices for creating clean air spaces during wildland fire smoke events. The meeting included presentations on the public health impacts of wildland fire smoke; public health agencies' experiences and resilience efforts; and methods to improve indoor air quality, including the effectiveness of air filtration methods [e.g., building heating ventilation and air conditioning (HVAC) systems and portable, free-standing air filtration systems]. These presentations and related research indicate that filtration has been demonstrated to effectively improve indoor air quality during high ambient air pollution events; however, several research questions remain regarding the longevity and maintenance of filtration equipment during and after smoke events, effects on the pollution mixture, and degree to which adverse health effects are reduced.

Indoor air quality assessment in a residential apartment in Budapest

2021 ◽  
Author(s):  
Bushra Atfeh ◽  
Erzsébet Kristóf ◽  
Róbert Mészáros ◽  
Zoltán Barcza
Keyword(s):  
Air Quality ◽  
Data Processing ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Low Cost ◽  
Carbon Dioxide Concentration ◽  
Monitoring Network ◽  
Sampling Frequency ◽  
Irregular Sampling ◽  
Air Quality Measurements

&lt;p&gt;This work focuses on indoor air quality measurements carried out in an apartment in the suburban region of Budapest. The measurements were made by an IQAir AirVisual node air quality monitor which is a so-called low-cost sensor capable to monitor PM&lt;sub&gt;2.5&lt;/sub&gt; and carbon dioxide concentration. In this study we analyze data measured during January 2017 that was characterized by an extreme air pollution episode in Budapest. The aim of the study was to calculate daily indoor PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations that are comparable with the outdoor concentrations provided by the official Hungarian Air Quality Monitoring Network. Given the fact that AirVisual Pro provides data with irregular sampling frequency, data processing is expected to influence the calculated daily mean concentrations.&amp;#160; The results indicated that the uneven sampling frequency characteristic of AirVisual node indeed causes problems during data processing and has an effect on the calculated means. We propose a &amp;#8216;best method&amp;#8217; for data processing for sensors with irregular sampling frequency.&lt;/p&gt;

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