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

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.

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

E3S Web of Conferences ◽

10.1051/e3sconf/20184400151 ◽

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.

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Epiphytic Cryptogams as Bioindicators of Air Quality in a Tropical Andean City

Sustainability ◽

10.3390/su132011218 ◽

2021 ◽

Vol 13 (20) ◽

pp. 11218

Author(s):

Juan Díaz ◽

Luis Montaño ◽

Paul Salinas ◽

Ángel Benítez

Keyword(s):

Air Pollution ◽

Air Quality ◽

Urban Areas ◽

Environmental Changes ◽

Low Cost ◽

Control Zone ◽

Air Quality Assessment ◽

Tropical Areas ◽

And Control ◽

The City

Air pollution is one of the main environmental problems in developed and developing countries. Epiphytic cryptogams (bryophytes and lichens) are proposed as a reliable indicator to detect environmental changes, given to their sensitivity to pollutants. In this study we evaluated air quality in the city of Ambato using bryophytes and epiphytic lichens on three land uses (urban, peri-urban and control). In each zone we selected ten trees (a total of 90 trees) for each station (a total of nine stations), where we recorded the frequency and cover of epiphytic cryptogams in a quadrat of 10 × 50 cm that was divided into 5 × 5 cm squares. Differences in richness, index of atmospheric purity (IAP) and diversity were analyzed using a generalized linear model (GLM) and changes in species composition using multivariate analysis. We recorded 39 species of cryptogams (25 lichens and 14 bryophytes). Richness, diversity and index of atmospheric purity were higher in the control zone compared to the urbanized zones. Community composition changed between the different zones, with increasing differences between the control and urban zones. The urban areas of the city of Ambato were identified with high levels of air pollution due to their lower diversity related to higher vehicular traffic and industrial activities (e.g., footwear and textile factories, tanneries). Thus, epiphytic cryptogams are a fast and low-cost method for air quality assessment in tropical areas.

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

10.5194/acp-2020-1169 ◽

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.

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High-resolution PM2.5 forecasting using CAMS predictions, low-cost sensors and ensemble techniques

10.5194/egusphere-egu21-15960 ◽

2021 ◽

Author(s):

Areti Pappa ◽

Ioannis Kioutsioukis

Keyword(s):

Air Pollution ◽

Air Quality ◽

Short Term Memory ◽

Low Cost ◽

Quality Models ◽

Ensemble Techniques ◽

Spatial Coverage ◽

Comparable Performance ◽

Lstm Network ◽

Air Quality Monitoring Stations

Expediting urbanization has triggered an increase in cardiopulmonary diseases attributable to fine-particulate air pollution. Air Quality models simulate the dilution and dispersion of air pollutants that affect the atmosphere, contributing crucially to the comprehension of its processes. Air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) provide open access to accurate and reliable information but in a coarse resolution. Data-driven models can downscale the forecasts from deterministic air quality models on the basis of reliable measurements. Low-cost air quality sensors are widely known for their increased spatial coverage and economic operational costs, but usually, their reliability is in dispute. In this study, a dense network of calibrated PM2.5 measurements installed in the city of Patras is combined with CAMS forecasts and statistical approaches to generate 24h forecasts of PM2.5 concentrations in an urban area of Greece. The implemented techniques are the analog ensemble (AnEn) and the Long Short-Term Memory (LSTM) network. Auxiliary variables of meteorological origin were also utilized. The required forecasts were retrieved from the European Center for Medium-Range Weather Forecasts (ECMWF), and were pin-pointed to the location of the air quality monitoring stations. The results showed that both methods had comparable performance, with low bias and relatively small errors. In the stations with high PM2.5 levels, AnEn performed better, whereas in the stations and seasons with moderate concentrations LSTM outperformed. A comprehensive validation is presented and discussed. AnEn and LSTM methods were proved reliable tools for air pollution forecasting and can be used for other regions with small modifications.

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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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FRI0531 AIR POLLUTANTS AND DEVELOPMENT OF INTERSTITIAL LUNG DISEASE IN PATIENTS WITH AUTOIMMUNE DISEASES

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.204 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 865.1-865

Author(s):

H. H. Chen ◽

W. C. Chao ◽

Y. H. Chen ◽

D. Y. Chen ◽

C. H. Lin

Keyword(s):

Air Quality ◽

Autoimmune Diseases ◽

Air Pollutants ◽

Air Pollutant ◽

Air Quality Monitoring ◽

Newly Diagnosed ◽

Science Technology ◽

Air Quality Monitoring Stations ◽

The Impact ◽

Modest Level

Background:Interstitial lung disease (ILD) is characterized by progressive inflammation and fibrosis, and accumulating evidence have shown that exposure to air pollutants was associated with the development of ILD. Autoimmune diseases are highly correlated with ILD, including connective tissue disease-associated ILD (CTD-ILD) as well as interstitial pneumonia with autoimmune features (IPAF), and the development of ILD is a crucial cause of morbidity and mortality in patients with autoimmune diseases. One recent Taiwanese study reported that exposure to air pollutants was associated with incident systemic lupus erythematosus (SLE). However, the impact of air pollutants on the development of ILD among patients with autoimmune diseases remains unknown.Objectives:The study aimed to address the impact of accumulating exposure to air pollutant above moderate level, defined by Air Quality Index (AQI) value higher than 50, on the development of ILD in patients with autoimmune diseases including SLE, rheumatoid arthritis (RA) and primary Sjögren’s syndrome (SS).Methods:We used a National Health Insurance Research Database in Taiwan to enroll patients with SLE (International Classification of Diseases (ICD)-9 code 710.0, n=13,211), RA (ICD-9 code 714.0 and 714.30–714.33, n=32,373), and primary SS (ICD-9 code, 710.0, n=15,246) between 2001 and 2013. We identified newly diagnosed ILD cases (ICD-code 515) between 2012 and 2013 and selected age, sex, disease duration and index-year matched (1:4) patients as non-ILD controls. The hourly levels of air pollutants one year prior to the index-date were obtained from 60 air quality monitoring stations across Taiwan, and the air pollutants in the present study consisted of particulate matter <2.5 μm in size (PM2.5), particulate matter <10 μm in size (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2) and ozone (O3). We used a spatio-temporal model built by a deep-learning mechanism to estimate levels of air pollutants at 374 residential locations based on data of 3 air quality monitoring stations near the location (8). Notably, we used cumulative exposed hours to air pollutants higher than modest level, defined by AQI criteria, given that daily mean level of air pollutants might possibly underestimate the triggered inflammatory effect by a temporary exposure of high-level air pollutant. A conditional logistic regression was used to determine the association between exposure to air pollutant and the development of ILD, adjusting age, gender, Charlson Comorbidity Index (CCI), urbanization, family income, and medications for autoimmune diseases.Results:A total of 272 patients with newly diagnosed ILD were identified among patients with autoimmune diseases, including 39 with SLE, 135 with RA, and 98 with primary SS. We found that the duration of exposure to PM 2.5 higher than modest level was associated with the risk of ILD development in patients with SS (adjOR 1.07, 95% CI 1.01–1.13), and similar trends were also found in patients with SLE (adjOR 1.03, 95% CI 0.95–1.12) and RA (adjOR 1.03, 95% CI 0.99–1.07). Intriguingly, we observed an inverse correlation between the duration of exposure to O3 and the development of ILD in patients with SS (adjOR 0.83, 95% CI 0.70–0.99); however, the finding was not found in patients with SLE (adjOR 1.13, 95% CI 0.92–1.37) and RA (adjOR 0.98, 95% CI 0.87–1.11).Conclusion:In conclusion, we identified that longer exposure to PM2.5 higher than modest level tended to be associated with the development of ILD in patients with autoimmune diseases, mainly SS.References:[1] Araki T, Putman RK, Hatabu H, Gao W, Dupuis J, Latourelle JC, et al. Development and Progression of Interstitial Lung Abnormalities in the Framingham Heart Study. Am J Respir Crit Care Med 2016;194:1514-1522.[2] Tang KT, Tsuang BJ, Ku KC, Chen YH, Lin CH, Chen DY. Relationship between exposure to air pollutants and development of systemic autoimmune rheumatic diseases: a nationwide population-based case-control study. Ann Rheum Dis 2019;78:1288-1291.Disclosure of Interests:Hsin-Hua Chen: None declared, Wen-Cheng Chao: None declared, Yi-Hsing Chen Grant/research support from: Taiwan Ministry of Science and Technology, Taiwan Department of Health, Taichung Veterans General Hospital, National Yang-Ming University, GSK, Pfizer, BMS., Consultant of: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Paid instructor for: Pfizer, Novartis, Johnson & Johnson, Roche, Lilly, Astra& Zeneca, Sanofi, Astellas, Agnitio Science Technology, United Biopharma., Speakers bureau: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Der-Yuan Chen: None declared, Ching-Heng Lin: None declared

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Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method

E3S Web of Conferences ◽

10.1051/e3sconf/201913605001 ◽

2019 ◽

Vol 136 ◽

pp. 05001 ◽

Cited By ~ 2

Author(s):

Ziyuan Ye

Keyword(s):

Air Quality ◽

Hybrid Model ◽

Air Pollutants ◽

Moving Average ◽

Mixing Time ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model ◽

Air Quality Monitoring Stations

In order to improve the accuracy of predicting the air pollutants in Shenzhen, a hybrid model based on ARIMA (Autoregressive Integrated Moving Average model) and prophet for mixing time and space relationships was proposed. First, ARIMA and Prophet method were applied to train the data from 11 air quality monitoring stations and gave them different weights. Then, finished the calculation about weight of impact in each air quality monitoring station to final results. Finally, built up the hybrid model and did the error evaluation. The result of the experiments illustrated that this hybrid method can improve the air pollutants prediction in Shenzhen.

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Low-Cost Air Quality Sensor Network in Munich

10.5194/egusphere-egu2020-19276 ◽

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

As the number of official monitoring stations for measuring urban air pollutants such as nitrogen oxides (NOx), particulate matter (PM) or ozone (O3) 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].We are currently establishing a low-cost, but high-accuracy network in Munich to measure the concentrations of NOx, PM, O3, 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.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. &#160;With the help of this approach, we will be able to create a calibrated real-time concentration map of air pollutants in Munich.[1] Bigi et al.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717&#8211;3735, 2018[2] Popoola et al., &#8220;Use of networks of low cost air quality sensors to quantify air quality in urban settings,&#8221; Atmos. Environ., 194, 58&#8211;70, 2018[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&#8211;247, 2017

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Design and Implementation of Portable Outdoor Air Quality Measurement System using Arduino

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i1.pp280-290 ◽

2018 ◽

Vol 8 (1) ◽

pp. 280 ◽

Cited By ~ 5

Author(s):

Teddy Surya Gunawan ◽

Yasmin Mahira Saiful Munir ◽

Mira Kartiwi ◽

Hasmah Mansor

Keyword(s):

Air Quality ◽

Measurement System ◽

Low Cost ◽

Public Awareness ◽

Quality Measurement ◽

Cost Effective ◽

Ground Level ◽

Air Pollutant ◽

Ground Level Ozone ◽

Air Quality Monitoring Stations

Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5μm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient.

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Daily variability of air pollution in locations of different population density

E3S Web of Conferences ◽

10.1051/e3sconf/201910000011 ◽

2019 ◽

Vol 100 ◽

pp. 00011

Author(s):

Robert Cichowicz ◽

Artur Stelęgowski

Keyword(s):

Air Pollution ◽

Air Quality ◽

Population Density ◽

Air Pollutants ◽

Daily Maximum ◽

Air Contaminants ◽

Daily Variability ◽

Air Quality Monitoring Stations ◽

Agricultural Areas ◽

Daily Changes

The air quality levels vary during a day, especially in inhabited areas. Therefore, it seems reasonable to observe and analyze the occurrence of daily maximum and minimum level of air pollution. In this article, data obtained from automatic air quality monitoring stations located in 5 large, 5 small and medium cities and 5 villages in Poland was analyzed in 2012−2016. Those locations vary, inter alia, depending on number of inhabitants and population density, and for this reason also due to the presence of air contaminants. As an indicator of daily variability air pollution it was determined the ratio of maximum to minimum concentrations of selected air pollutants (NO2 and NOx, and O3, SO2, CO, PM10 and PM2.5, and benzene) in urban and agricultural areas. In winter, the daily changes were bigger in cities than in villages. While in summer, the level of daily variability was similar, irrespective of size of the settlement unit. The biggest daily changes concerned nitrogen oxides, the lowest − sulfur dioxide and dusts.

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