Ambient Air Quality Measurement with Low-Cost Optical and Electrochemical Sensors: An Evaluation of Continuous Year-Long Operation

Sensor technology is attractive to the public due to its availability and ease of use. However, its usage raises numerous questions. The general trustworthiness of sensor data is widely discussed, especially with regard to accuracy, precision, and long-term signal stability. The VSB-Technical University of Ostrava has operated an air quality sensor network for more than two years, and its large sets of valid results can help in understanding the limitations of sensory measurement. Monitoring is focused on the concentrations of dust particles, NO2, and ozone to verify the impact of newly planted greenery on the reduction in air pollution. The sensor network currently covers an open field on the outskirts of Ostrava, between Liberty Ironworks and the nearby ISKO1650 monitoring station, where some of the worst air pollution levels in the Czech Republic are regularly measured. In the future, trees should be allowed to grow over the sensors, enabling assessment of the green barrier effect on air pollution. As expected, the service life of the sensors varies from 1 to 3 years; therefore, checks are necessary both prior to the measurement and regularly during operation, verifying output stability and overall performance. Results of the PMx sensory measurements correlated well with the reference method. Concentration values measured by NO2 sensors correlated poorly with the reference method, although timeline plots of concentration changes were in accordance. We suggest that a comparison of timelines should be used for air quality evaluations, rather than particular values. The results showed that the sensor measurements are not yet suitable to replace the reference methods, and dense sensor networks proved useful and robust tools for indicative air quality measurements (AQM).

Download Full-text

Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States

10.5194/amt-2016-131 ◽

2016 ◽

Cited By ~ 5

Author(s):

Wan Jiao ◽

Gayle Hagler ◽

Ronald Williams ◽

Robert Sharpe ◽

Ryan Brown ◽

...

Keyword(s):

Air Pollution ◽

Carbon Monoxide ◽

Air Quality ◽

Sulfur Dioxide ◽

Sensor Network ◽

Correlation Coefficient ◽

Low Cost ◽

Ambient Air ◽

Sensor Technology ◽

Sensor Performance

Abstract. Advances in air pollution sensor technology have enabled the development of small and low cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low cost, continuous and commercially-available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ~ 2 km area in Southeastern U.S. Co-location of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as whether multiple identical sensors reproduced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (2 nodes) and PM (4 nodes) data for an 8 month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to near-by traffic emissions. Overall, this study demonstrates a straightforward methodology for establishing low-cost air quality sensor performance in a real-world setting and demonstrates the feasibility of deploying a local sensor network to measure ambient air quality trends.

Download Full-text

Air Pollution and Lung Capacity of People Living around the Cement Industry

Public Health of Indonesia ◽

10.36685/phi.v2i2.69 ◽

2016 ◽

Vol 2 (2) ◽

pp. 76-83

Author(s):

Erwin Azizi Jayadipraja ◽

Anwar Daud ◽

Alimuddin Hamzah Assegaf ◽

Maming

Keyword(s):

Air Pollution ◽

Air Quality ◽

Ambient Air ◽

Quality Standard ◽

Cement Industry ◽

Anthropogenic Sources ◽

Dust Particles ◽

Ambient Air Quality ◽

Cross Sectional ◽

Lung Capacity

Backgrounds: A cement industry is one of anthropogenic sources of air pollution. In polluting the air, the industry creates some dust particles, nitrogen oxide (NO2), sulfur oxide (SO2), and carbon monoxide (CO).Research Purpose: The research aims at finding out the ambient air quality around a cement industry and relating it with the lung capacity of people living around the area.Methodology: This research uses cross sectional studies by measuring the ambient air quality in the morning, noon, and evening in four different settlements within 3 km from the cement industry. The measurement is then correlated with the FEV1 and FVC of lung capacity of people living around the area.Result: Of all four locations, three have ambient air quality (PM2.5 = 109.47 µg/Nm3, TSP = 454.7 µg/Nm3) that surpass the quality standard (PM2.5 = 65 µg/Nm3, TSP = 230 µg/Nm3). Of 241 respondents, the average level of FVC and FEV1 is respectively 1.9352 liter (SD: 0.45578) and 1.7486 liter (SD: 0.43874). Furthermore, the level of PM2.5 in the morning and at noon is respectively p=0.009 and p=0.003; the level of TSP in the morning and at noon is respectively p=0.003 and p=0.01; the level of NO2 in the morning is p=0.006; the level of SO2 in the morning, at noon and in the evening is respectively p=0.000, p=0.022, and p=0.000; and the level of CO in the morning, at noon and in the evening is respectively p=0.003, p=0.015, and p=0.024. Those levels are associated with the level of respondents’ FEV1. Moreover, the level of TSP in the morning is p=0.024; the level of SO2 in the morning and in the evening is p=0.007. These levels relate to the level of respondents’ FVC.Keywords: FVC, FEV1, CO, NO2, SO2, TSP, PM2.5, cement industry.

Download Full-text

Air Quality and Human Health among a Low Income Community in the Highveld Priority Area

Clean Air Journal ◽

10.17159/caj/2011/20/1.7180 ◽

2011 ◽

Vol 20 (1) ◽

Author(s):

C.Y Wright ◽

R Oosthuizen ◽

J John ◽

R.M Garland ◽

P Albers ◽

...

Keyword(s):

South Africa ◽

Air Pollution ◽

Air Quality ◽

Human Health ◽

Low Income ◽

Power Plants ◽

Ambient Air ◽

Agricultural Activity ◽

Priority Area ◽

The Impact

Human exposure to poor air quality is linked to adverse health effects. The largest burden of air pollution-related diseases is in developing countries where air pollution levels are also among the highest in the world. In South Africa, two geographic areas, the Vaal Triangle and the Highveld, have been identified for air quality managementinterventions to ensure compliance with National Air Quality Management Standards and to control potential harmful air pollution impacts on human health. The Highveld Priority Area (HPA) is characterised by intense mining, coal-fired power plants, industries, including iron and steel manufacturing, chemical plants, agricultural activity, motor vehicles and domestic fuel burning. Apart from two previous studies, no respiratory health studies have been carried out in the HPA. This paper describes the results of a recent, comprehensive study of ambient air quality, potential exposure to air pollution and air-related human health among a low income community living in the HPA in order to better understand the impact of air pollution on human health in South Africa.

Download Full-text

Breathe Easy, There's an App for That: Using Information and Communication Technology to Avoid Air Pollution in Bogotá

10.18235/0003725 ◽

2021 ◽

Author(s):

Allen Blackman ◽

Bridget Hoffmann

Keyword(s):

Air Pollution ◽

Air Quality ◽

Real Time ◽

Avoidance Behavior ◽

Ambient Air ◽

Text Messages ◽

Information And Communication ◽

Real Time Information ◽

The Impact ◽

Time Information

Ambient air pollution is a leading cause of death in developing countries. In theory, using smartphone apps, text messages, and other personal information and communication technologies to disseminate real-time information about such pollution can boost avoidance behavior like wearing face masks and closing windows. Yet evidence on their effectiveness is limited. We conduct a randomized controlled trial to evaluate the impact of training university students in Bogotá, Colombia to use a newly available municipal government smartphone app that displays real-time information on air quality. The training increased participants acquisition of information about air quality, their knowledge about avoidance behavior, and their actual avoidance behavior. It also enhanced their concern about other environmental issues. These effects were moderated by participants characteristics. For example, the training was generally less effective among job holders.

Download Full-text

The Impact of Air Quality on Inbound Tourist Arrivals over China Based on Grey Relational Analysis

Sustainability ◽

10.3390/su131910972 ◽

2021 ◽

Vol 13 (19) ◽

pp. 10972

Author(s):

Wei Zhang ◽

Ziqiang Liu ◽

Yujie Zhang ◽

Elly Yaluk ◽

Li Li

Keyword(s):

Air Pollution ◽

Air Quality ◽

Quality Indicators ◽

Grey Relational Analysis ◽

Pollution Index ◽

Ambient Air ◽

Relational Analysis ◽

Grey Relational ◽

The Impact ◽

Relational Order

Air pollution has a significant impact on tourism; however, research in this area is still limited. In this study, we applied grey relational analysis to panel data from 31 provinces in China and evaluated the relationship between air quality and inbound tourist arrivals. The study focused on provincial-level disparities for the different key air quality evaluation standards during 2009–2012 and 2013–2019. For instance, we considered PM10, SO2, NO2 and the excellent and good ratings of Air Pollution Index (API) during 2009–2012 and the additional PM2.5, CO, O3 and the excellent and good ratings of Air Quality Index (AQI) from 2013 to 2019. Results indicate that: (1) Inbound tourist arrivals are significantly and positively affected by ambient air quality, and the impact from 2013 to 2019 was greater than that from 2009 to 2012; (2) there is regional diversity in inbound tourist arrivals, and the impact of the different air quality indicators varies; (3) inbound tourists showed greater sensitivity to air pollution under the AQI standard; (4) the impact of air quality indicators on the inbound tourist arrivals shows grey relational order, and the concentration of PM2.5, PM10 and SO2 have less impact than NO2, CO and O3 on changes in tourism numbers; (5) consistency in the air quality impact on foreign tourists and compatriot tourists from HK, MO and TW varies by air quality indicators. This study highlights the need for appropriate measures to improve air quality for high-quality and sustainable development of inbound tourism.

Download Full-text

A mitigation study for air pollution management across Germany for NOX (NO + NO2) with the LOTOS-EUROS CTM – Part I: Comparing the labeling and brute force technique for source attribution.

10.5194/egusphere-egu21-5862 ◽

2021 ◽

Author(s):

Markus Thürkow ◽

Joscha Pültz ◽

Martijn Schaap

Keyword(s):

Air Pollution ◽

Air Quality ◽

Ambient Air ◽

Brute Force ◽

Ambient Air Quality ◽

Source Attribution ◽

Application Range ◽

Impact Studies ◽

Non Linear ◽

The Impact

Air quality is a key aspect of present environmental discussions with nitrogen oxides (NOX = NO + NO2) has become a decisive element and impact factor for air quality planning. Millions of people are exposed by NO2, especially in urban areas near traffic sites, leading to increased mortality rates. As the annual limit value of 40 &#956;g/m3, introduced by the European Ambient Air Quality Directive (EC, 2008), is currently exceeded by about 39 % (UBA, 2019), in Germany an estimated number of 13.100 premature deaths are caused by NO2 (EEA, 2018). The origin and formation processes of NOX are well documented in literature for long: NO mainly originates from incomplete combustion (Granier et al., 2011; Vestreng et al., 2009), with NO2 formed as a photochemical reaction product (Finlayson-Pitts and Pitts, 2000; Leighton, 1961). Therefore, to further improve the ambient air quality using cost-effective mitigation strategies, this requires for quantifying the contribution of the ambient air pollution by source sectors and regions of their origin (Belis et al., 2020).Applying chemical transport models (CTMs) for source attribution (SA), one can distinguish between contributions and impacts. Methods to estimate contributions are known as labeling (Kranenburg et al., 2013) or tagging (Wang et al., 2009; Wagstrom et al., 2008) approaches and are based on conservation of mass. In contrast, sensitivity simulations, such as the top-down brute force (BF) technique, can be used to quantify the impact to different emission reductions (Clappier et al., 2017; Thunis et al., 2019). As the BF approach in theory is only designed for impact studies, the calculation of contributions can result in incorrect estimates which is dependent on the linearity of the considered component (Clappier et al., 2017; Thunis et al., 2019). Therefore, impact studies can only be employed under certain restrictions and their application range needs to be predefined first (Thunis et al., 2020).Previous studies primarily focused on PM when comparing different approaches for SA. Therefore, we conducted a SA study by performing air pollution simulations using the LOTOS-EUROS CTM across Germany of January 1st to December 31st, 2018 for NOX. We enhanced the understanding of limitations to non-linear interaction terms and defined the potential application range for SA purposes using impact studies of NOX, by comparing the labeling approach implemented in the LOTOS-EUROS CTM to the BF technique.First results indicate that impact studies cannot be used to estimate contributions of NO due to their non-linear relations and inconsistent mass conservation. Even though differences for NO2 are smaller, it is not recommended to apply the BF technique here either. However, considering that non-emission sources cannot be separated from each other in impact studies, it is further advised not to apply this method for NOX.

Download Full-text

Impacts of household sources on air pollution at village and regional scales in India

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-7719-2019 ◽

2019 ◽

Vol 19 (11) ◽

pp. 7719-7742 ◽

Cited By ~ 7

Author(s):

Brigitte Rooney ◽

Ran Zhao ◽

Yuan Wang ◽

Kelvin H. Bates ◽

Ajay Pillarisetti ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Chemical Transport ◽

Ambient Air ◽

Household Air Pollution ◽

World Health ◽

New Delhi ◽

Computational Domain ◽

Surveillance Site ◽

The Impact

Abstract. Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM2.5 and O3) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM2.5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM2.5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µg m−3, and, on occasion, exceeded 180 µg m−3. PM2.5 levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM2.5 levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM2.5 produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM2.5 during the periods studied at the four locations averaged about 30 µg m−3, representing approximately 30 % and 20 % of total PM2.5 levels in the rural and urban stations, respectively.

Download Full-text

Analysis of existing air monitoring technologies

System technologies ◽

10.34185/1562-9945-3-134-2021-08 ◽

2021 ◽

Vol 3 (134) ◽

pp. 67-78

Author(s):

Volodymyr Tarasov ◽

Bohdan Molodets ◽

Тatyana Bulanaya ◽

Oleg Baybuz

Keyword(s):

Air Pollution ◽

Air Quality ◽

Quality Index ◽

Ambient Air ◽

Human Condition ◽

Air Quality Index ◽

Air Monitoring ◽

Atmospheric Air ◽

Monitoring Technologies ◽

The Impact

Atmospheric air monitoring is a systematic, long-term assessment of the level of certain types of pollutants by measuring their amount in the open air. Atmospheric air monitoring is an integral part of an effective air quality management system and is carried out through environmental monitoring networks, which should support timely provision of public information about air pollution, support compliance with ambient air quality standards and development of emission strategies, support for air pollution research.The work is devoted to existing air monitoring technologies: ground (sensors, diffusion tubes, etc.) and remote resources (satellites, aircraft, etc.). In addition, standards of air quality assessment (European and American) are described. As an example, we consider the European Air Quality Index (EAQI) and the Air Quality Index according to EPF standards: indicators by which these indices are calculated, the ranking of air status depending on the value of the index are described.AQI (Air Quality Index) is used as an indicator of the impact of air on the human condition. The European Air Quality Index allows users to better understand air quality where they live, work or travel. By displaying information for Europe, users can gain an understanding of air quality in individual countries, regions and cities. The index is based on the values of the concentration of the five main pollutants, including particles less than 10μm (PM10), particles less than 2.5μm (PM2.5), ozone (O3); nitrogen dioxide (NO2); sulfur dioxide (SO2). To conclude, ground stations give a more accurate picture of the state of the air at a point, while satellite image data with a certain error (due to cloud cover, etc.) can cover a larger area and solve the problem of coverage of stations in the area. There is no single standard for calculation. Today, the European Air Quality Index (EAQI) is used in Ukraine and Europe.

Download Full-text

The Causal Effect of Air Pollution on COVID-19 Transmission: Evidence from China

10.1101/2020.10.19.20215236 ◽

2020 ◽

Author(s):

Guojun He ◽

Yuhang Pan ◽

Takanao Tanaka

Keyword(s):

Air Pollution ◽

Air Quality ◽

Causal Effect ◽

Ambient Air ◽

Ambient Air Pollution ◽

Percentage Point Increase ◽

Distributed Lag ◽

Using Data ◽

Epidemiological Characteristics ◽

The Impact

There is increasing concern that ambient air pollution could exacerbate COVID-19 transmission. However, estimating the relationship is challenging because it requires one to account for epidemiological characteristics, to isolate the impact of air pollution from potential confounders, and to capture the dynamic impact. We propose a new econometric framework to address these challenges: we rely on the epidemiological Susceptible-Infectious-Recovered-Deceased (SIRD) model to construct the outcome of interest, the Instrument Variable (IV) model to estimate the causal relationship, and the Flexible-Distributed-Lag (FDL) model to understand the dynamics. Using data covering all prefectural Chinese cities, we find that a 10-point (14.3%) increase in the Air Quality Index would lead to a 2.80 percentage point increase in the daily COVID-19 growth rate with 2 to 13 days of delay (0.14 ∼ 0.22 increase in the reproduction number: R0). These results imply that improving air quality can be a powerful tool to contain the spread of COVID-19.

Download Full-text

Assessment of the Effects of the Spanish National Air Pollution Control Programme on Air Quality

Atmosphere ◽

10.3390/atmos12020158 ◽

2021 ◽

Vol 12 (2) ◽

pp. 158 ◽

Cited By ~ 1

Author(s):

Marta G. Vivanco ◽

Juan Luis Garrido ◽

Fernando Martín ◽

Mark R. Theobald ◽

Victoria Gil ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Urban Areas ◽

Transport Model ◽

Ambient Air ◽

Control Programme ◽

Mitigation Measures ◽

Nox Emissions ◽

Air Pollution Control ◽

The Impact

During the last few decades, European legislation has driven progress in reducing air pollution in Europe through emission mitigation measures. In this paper, we use a chemistry transport model to assess the impact on ambient air quality of the measures considered for 2030 in the for the scenarios with existing (WEM2030) and additional measures (WAM2030). The study estimates a general improvement of air quality for the WAM2030 scenario, with no non-compliant air quality zones for NO2, SO2, and PM indicators. Despite an improvement for O3, the model still estimates non-compliant areas. For this pollutant, the WAM2030 scenario leads to different impacts depending on the indicator considered. Although the model estimates a reduction in maximum hourly O3 concentrations, small increases in O3 concentrations in winter and nighttime in the summer lead to increases in the annual mean in some areas and increases in other indicators (SOMO35 for health impacts and AOT40 for impacts on vegetation) in some urban areas. The results suggest that the lower NOx emissions in the WEM and WAM scenarios lead to less removal of O3 by NO titration, especially background ozone in winter and both background and locally produced ozone in summer, in areas with high NOx emissions.

Download Full-text