Algal biofilms on tree bark to monitor airborne pollutants

Biologia ◽  
2008 ◽  
Vol 63 (6) ◽  
Author(s):  
Katharina Freystein ◽  
Mario Salisch ◽  
Werner Reisser
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Green Algae ◽  
Algal Species ◽  
Pollution Monitoring ◽  
Tree Bark ◽  
Airborne Pollutants ◽  
Morphological Studies ◽  
Autotrophic Biofilms ◽  
Pm 10

AbstractAlgae are used in biomonitoring systems to detect water or soil pollution. So it is conceivable to establish a biomonitoring system for the detection of airborne pollutants (ozone and particulate matter (PM-10)) in urban habitats by algae. Autotrophic biofilms are widely present, cover nearly every exposed surface, especially tree bark and consist of a large variety of species of algae, cyanobacteria and fungi. To explore the diversity of green algae at different air pollution monitoring sites we choose trees with different structures of bark at three locations in and near Leipzig. We compared the measured levels of air pollution with the algal species and communities present. The sites differed in the quality and amount of airborne pollutants, among which we concentrated on ozone and particulate matter (PM-10). The collection sites were Leipzig-Centre, Leipzig-West and a forest area east of Leipzig (Collmberg). Autotrophic biofilms were collected, algae cultures established and taxonomic and morphological studies were carried out with light microscopy. Green algae were present on tree bark at all sites and forty-eight different algal species and cyanobacteria were isolated. Preliminary results suggested a correlation between pollutants and occurrence of some specific algal species and the specific algal assemblages at a given site. It is concluded that this could provide the basis for a biomonitoring system involving aero-terrestrial algae for the detection of airborne pollutants.

scholarly journals Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

2020 ◽  
Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Additive Models ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 µg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

scholarly journals Using Ultrasound-Treated Washout from Conifer Needles and Fresh Snow Samples in Air Pollution Monitoring

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
A. Kholodov ◽  
M. Tretyakova ◽  
K. Golokhvast
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Ambient Air ◽  
Atmospheric Particulate Matter ◽  
Pollution Monitoring ◽  
Ambient Air Pollution ◽  
Air Pollution Monitoring ◽  
Conifer Needles ◽  
Fresh Snow ◽  
Snow Precipitation

Snow precipitation and snowpack are commonly used to assess the condition of the aerial environment. Another way to monitor air quality is to study trees and shrubs, which are natural barriers for capturing air pollution, including atmospheric particulate matter. The hypothesis of the current study was that using fresh snow precipitation and washout from vegetation for the monitoring of air pollution can produce comparable results. In this study, we compared the results of laser diffraction analysis of suspended particular matter in melted fresh snow and ultrasound-treated washout from conifer needles. The samples were collected at several sites in Primorsky Krai, Russian Federation, and analyzed according to the same scheme. We observed that the content of particulate matter with a smaller aerodynamic diameter in the ultrasound-treated washout from conifer needles was higher than that in the melted fresh snow. The content of PM10 in the ultrasound-treated washout from conifers was increased by 6–27% depending on the site, showing greater efficacy of this method. This method can be used as an alternative to the sampling of snow for the monitoring of ambient air pollution, taking into account several limitations.

scholarly journals Trend of Particulate Matter PM 2.5 and PM 10 in Dhaka City

1970 ◽  
Vol 46 (3) ◽  
pp. 389-398 ◽  
Author(s):  
MA Rouf ◽  
M Nasiruddin ◽  
AMS Hossain ◽  
MS Islam
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Winter Season ◽  
Ambient Air ◽  
Quality Data ◽  
Dhaka City ◽  
Monsoon Period ◽  
Pm 2.5 ◽  
Pm 10

Dhaka City has been affecting with severe air pollution particularly by particulate matter. The ambient air quality data for particulate matter were collected during April 2002 to September 2005 at the Continuous Air Quality Monitoring Station (CAMS) located at Sangshad Bhaban, Dhaka. Data reveal that the pollution from particulate matter greatly varies with climatic condition. While the level comes down the limit value in the monsoon period (April-October), it goes beyond the limit during non-monsoon time (November-March). The latest data show that during monsoon period PM 10 concentration varies from 50 μg/m3 to 80 μg/m3 and PM 2.5 concentration from 20 μg/m3 to 60 μg/m3 and during non monsoon period PM 10 varies from 100 μg/m3 to 250 μg/m3 and PM 2.5 varies from 70 μg/m3 to 165 μg/m3. The seasonal variation clearly indicates the severe PM 10 pollution during the dry winter season and also sometime during post-monsoon season in Dhaka City. Keywords: Air pollution; PM 2.5; PM 10; Air quality DOI: http://dx.doi.org/10.3329/bjsir.v46i3.9049 BJSIR 2011; 46(3): 389-398

Evaluation of Air Pollution by NO2, SO2, PM10 in Bucharest

2018 ◽  
Vol 69 (1) ◽  
pp. 105-111
Author(s):  
Carmen Otilia Rusanescu ◽  
Cosmin Jinescu ◽  
Marin Rusanescu ◽  
Mihaela Begea ◽  
Olimpia Ghermec
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Sulfur Dioxide ◽  
Urban Areas ◽  
Average Concentration ◽  
Limit Set ◽  
Temperature Influence ◽  
Wind Regime ◽  
Pm 10

In this paper is analysed the air quality in urban areas in Bucharest, the analysis was based on the monitoring of the average concentration of particulate matter PM 10, nitrogen oxides, NO2, and sulfur dioxide, SO2 in Bucharest between 2009-2015. The analysis refers to the maximum concentration of 24 h and the occurrence of overruns beyond the limit set. It also looked at the wind regime, air quality and temperature influence on air pollution in Bucharest between 2009-2015.

scholarly journals Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia

2018 ◽  
Vol 10 (8) ◽  
pp. 1317 ◽  
Author(s):  
Meredith Franklin ◽  
Khang Chau ◽  
Olga Kalashnikova ◽  
Michael Garay ◽  
Temuulen Enebish ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Capital City ◽  
World Health ◽  
Dust Particles ◽  
Support Vector ◽  
Daily Maximum ◽  
Pm 2.5 ◽  
Pm 10

Ulaanbaatar (UB), the capital city of Mongolia, has extremely poor wintertime air quality with fine particulate matter concentrations frequently exceeding 500 μg/m3, over 20 times the daily maximum guideline set by the World Health Organization. Intensive use of sulfur-rich coal for heating and cooking coupled with an atmospheric inversion amplified by the mid-continental Siberian anticyclone drive these high levels of air pollution. Ground-based air quality monitoring in Mongolia is sparse, making use of satellite observations of aerosol optical depth (AOD) instrumental for characterizing air pollution in the region. We harnessed data from the Multi-angle Imaging SpectroRadiometer (MISR) Version 23 (V23) aerosol product, which provides total column AOD and component-particle optical properties for 74 different aerosol mixtures at 4.4 km spatial resolution globally. To test the performance of the V23 product over Mongolia, we compared values of MISR AOD with spatially and temporally matched AOD from the Dalanzadgad AERONET site and find good agreement (correlation r = 0.845, and root-mean-square deviation RMSD = 0.071). Over UB, exploratory principal component analysis indicates that the 74 MISR AOD mixture profiles consisted primarily of small, spherical, non-absorbing aerosols in the wintertime, and contributions from medium and large dust particles in the summertime. Comparing several machine learning methods for relating the 74 MISR mixtures to ground-level pollutants, including particulate matter with aerodynamic diameters smaller than 2.5 μm ( PM 2.5 ) and 10 μm ( PM 10 ), as well as sulfur dioxide ( SO 2 ), a proxy for sulfate particles, we find that Support Vector Machine regression consistently has the highest predictive performance with median test R 2 for PM 2.5 , PM 10 , and SO 2 equal to 0.461, 0.063, and 0.508, respectively. These results indicate that the high-dimensional MISR AOD mixture set can provide reliable predictions of air pollution and can distinguish dominant particle types in the UB region.

AMBIENT PARTICULATE MATTER (PM10 AND PM2.5) AIR POLLUTION IN DESNIANSKY DISTRICT IN KYIV

2017 ◽  
Vol 2017 (67) ◽  
pp. 31-37
Author(s):  
O. Turos ◽  
◽  
T. Maremukha ◽  
I. Kobzarenko ◽  
A. Petrosian ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Ambient Particulate Matter ◽  
Pm10 And Pm2.5
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