scholarly journals Lichens as bioindicators of air quality in Dimitrovgrad (South-Eastern Serbia)

2010 ◽  
Vol 62 (3) ◽  
pp. 643-648 ◽  
Author(s):  
S. Stamenkovic ◽  
M. Cvijan ◽  
Mirjana Arandjelovic

Air pollution detection in Dimitrovgrad has not been done yet. In this work different lichen have been used as a bioindication to establish different air pollution levels. At 18 investigated points 22 lichen taxa have been found. Using the Index of Atmospheric Purity (IAP) it has been found that there are 3 different air pollution zones in Dimitrovgrad: 'lichen desert', 'transitional' and 'normal zone'. The most sensitive lichen taxa in Dimitrovgrad are Evernia prunastra, Ochrolechia pallescens, Parmelia sulcata, and Physcia tenella and the most tolerant are Phaeophyscia orbicularis, Physcia adscendens, Physconia distorta, Physconia grisea, and Xanthoria parietina. .

2004 ◽  
Vol 56 (3-4) ◽  
pp. 139-143 ◽  
Author(s):  
S. Stamenkovic ◽  
Mirko Cvijan

Epiphytic lichens were used dor bioindication of air pollution in the city of Vranje for the first time. In 43 investigated places, the authors established the presence of 38 lichen taxa from 20 genera. By calculating the index of atmospheric purity (IAP), they established three zones with different air pollution levels (a "lichen desert zone", a "transitional zone", and a "normal zone") and two subzones (a "narrow transitional zone", and a "wider transitional zone"). The species Melanelia exasperatula, M. glabra, Lecanora allophana, Lecidella elaechroma, Ochrolechia sp, Ramalina sp., and Parmelina quercina were established as the most sensitive. On the other hand, Phaeophyscia orbicularis, Xanthoria parietina, and Parmelia sulcata were the most tolerant.


2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
G. Lancia ◽  
F. Rinaldi ◽  
P. Serafini

We describe mathematical models and practical algorithms for a problem concerned with monitoring the air pollution in a large city. We have worked on this problem within a project for assessing the air quality in the city of Rome by placing a certain number of sensors on some of the city buses. We cast the problem as a facility location model. By reducing the large number of data variables and constraints, we were able to solve to optimality the resulting MILP model within minutes. Furthermore, we designed a genetic algorithm whose solutions were on average very close to the optimal ones. In our computational experiments we studied the placement of sensors on 187 candidate bus routes. We considered the coverage provided by 10 up to 60 sensors.


2019 ◽  
Vol 5 (3) ◽  
pp. 205630511986765
Author(s):  
Supraja Gurajala ◽  
Suresh Dhaniyala ◽  
Jeanna N. Matthews

Poor air quality is recognized as a major risk factor for human health globally. Critical to addressing this important public-health issue is the effective dissemination of air quality data, information about adverse health effects, and the necessary mitigation measures. However, recent studies have shown that even when public get data on air quality and understand its importance, people do not necessarily take actions to protect their health or exhibit pro-environmental behaviors to address the problem. Most existing studies on public attitude and response to air quality are based on offline studies, with a limited number of survey participants and over a limited number of geographical locations. For a larger survey size and a wider set of locations, we collected Twitter data for a period of nearly 2 years and analyzed these data for three major cities: Paris, London, and New Delhi. We identify the three hashtags in each city that best correlate the frequency of tweets with local air quality. Using tweets with these hashtags, we determined that people’s response to air quality across all three cities was nearly identical when considering relative changes in air pollution. Using machine-learning algorithms, we determined that health concerns dominated public response when air quality degraded, with the strongest increase in concern being in New Delhi, where pollution levels are the highest among the three cities studied. The public call for political solutions when air quality worsens is consistent with similar findings with offline surveys in other cities. We also conducted an unsupervised learning analysis to extract topics from tweets in Delhi and studied their evolution over time and with changing air quality. Our analysis helped extract relevant words or features associated with different air quality–related topics such as air pollution policy and health. Also, the topic modeling analysis revealed niche topics associated with sporadic air quality events, such as fireworks during festivals and the air quality impact on an outdoor sport event. Our approach shows that a tweet-based analysis can enable social scientists to probe and survey public response to events such as air quality in a timely fashion and help policy makers respond appropriately.


2007 ◽  
Vol 121 (1) ◽  
pp. 17 ◽  
Author(s):  
M. Elsinger ◽  
E. Burrell ◽  
N. DeBruyn ◽  
K. Tanasichuk ◽  
K. Timoney

Lichens that grow on the bark of mature trees were studied at 35 sites along an air pollution gradient east of Edmonton, Alberta. Data on species composition, richness, and cover were recorded in October 1999 in a matrix of sites that extends from a known source of pollutants (the Strathcona Industrial Area) east across Strathcona County. Air pollution is affecting the corticolous lichen community. Lichen species richness and total cover increased with distance from the pollution source. Species richness in areas distant from pollution was roughly twice that in areas near the Strathcona Industrial Area. Xanthoria fallax and Phaeophyscia orbicularis were the most pollution tolerant lichens. Xanthoria hasseana, Ochrolechia arborea, Physcia adscendens, Parmelia sulcata, and Melanelia albertana were rare or absent near the pollution source and common in more distant areas. Most of the 15 species assessed were sensitive to air quality to some degree. Some lichens near the refineries and in Sherwood Park showed abnormal coloration and poor thallus integrity indicative of stress. We discuss implications for human health.


2017 ◽  
Vol 2634 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Weibo Li ◽  
Maria Kamargianni

A modal shift from motorized to nonmotorized vehicles is imperative to reduce air pollution in developing countries. Nevertheless, whether better air quality will improve the willingness to use nonmotorized transport remains unclear. If such a reciprocal effect could be identified, a sort of virtuous circle could be created (i.e., better air quality could result in higher nonmotorized transport demand, which in turn could further reduce air pollution). Developing countries may, therefore, be more incentivized to work on air pollution reduction from other sources to exploit the extra gains in urban transport. This study investigated the impact of air pollution on mode choices and whether nonmotorized transport was preferred when air quality was better. Revealed preference data about the mode choice behavior of the same individuals was collected during two seasons (summer and winter) with different air pollution levels. Two discrete mode choice models were developed (one for each season) to quantify and compare the impacts of different air pollution levels on mode choices. Trip and socioeconomic characteristics also were included in the model to identify changes in their impacts across seasons. Taiyuan, a Chinese city that operates a successful bikesharing scheme, was selected for a case study. The study results showed that air quality improvement had a significant, positive impact on nonmotorized transport use, which suggested that improvements in air quality and promotion of nonmotorized transport must be undertaken simultaneously because of their interdependence. The results of the study could act as a harbinger to policy makers and encourage them to design measures and policies that lead to sustainable travel behavior.


Humankind, moving to a period centered upon improvement has overlooked the significance of supportability and has been the real guilty party behind the rising Pollution levels in the world's air among all other living life forms. The Pollution levels at certain spots have come to such high degrees that they have begun hurting our very own It will being. An IoT based Air Pollution observing framework incorporates a MQ Series sensor interfaced to a Node MCU outfitted with an ESP8266 WLAN connector to send the sensor perusing to a Thing Speak cloud. Further extent of this work incorporates an appropriate AI model to foresee the air Pollution level and an anticipating model, which is fundamentally a subset of prescient displaying. As age of poisonous gases from ventures, vehicles and different sources is immensely expanding step by step, it winds up hard to control the dangerous gases from dirtying the unadulterated air. In this paper a practical air Pollution observing framework is proposed. This framework can be utilized for observing Pollutions in demeanor of specific territory and to discover the air peculiarity or property examination. The obligated framework will concentrate on the checking of air poisons concentrate with the assistance of mix of Internet of things with wireless sensor systems. The investigation of air quality should be possible by figuring air quality index (AQI)


2021 ◽  
Author(s):  
Wojciech Nazar ◽  
Katarzyna Plata-Nazar

Abstract Background Decreased air quality is connected to a higher number of hospital admissions and an increase in daily mortality rates. Thus, Poles’ behavioural response to sometimes elevated air pollution levels is vital. The aim of this study was to carry out analysis of changes in air-pollution related information seeking behaviour in response to nationwide reported air quality in Poland. Methods Google Trends Search Volume Index data was used to investigate Poles’ interest in air pollution-related keywords. PM10 and PM2.5 concentrations measured across Poland between 2016 and 2019 were collected from the Chief Inspectorate of Environmental Protection databases. Pearson Product-Moment Correlation and the R2 correlation coefficient of determination were used to measure spatial and seasonal correlations between reported air pollution levels and the popularity of search queries. Results The highest PM10 and PM2.5 concentrations were observed in southern voivodeships and during the winter season. Similar trends were observed for Poles’ interest in air-pollution related keywords. All R2 coefficient of determination values were > 0.5 and all correlations were statistically significant. Conclusion Poland’s air quality does not meet the World Health Organisation guidelines. Also, the air quality is lower in southern Poland and during the winter season. It appears that Poles are aware of this issue and search for daily air quality data in their location. Greater interest in air quality data in Poland strongly correlates with both higher regional and higher seasonal air pollution levels.


2021 ◽  
Author(s):  
Angelika Heil ◽  
Augustin Colette

<p>Air quality forecasts help decision-makers to respond to air pollution episodes and to improve air quality management. In recent years, the public increasingly uses mobile apps to check forecasted air pollution levels and then adjusts outdoor activities accordingly. For Europe, state-of-the-art daily air quality forecasts are provided by the regional Copernicus Atmosphere Monitoring System (CAMS). The system integrates forecasts from 9 individual models. This ensemble approach not only achieves better predictive performance compared to a single model, but also allows a better quantification of forecast uncertainty. How to best communicate this uncertainty to a broad audience is by no means a trivial task, but yet essential to maintain trust in the forecasts.</p><p>We developed innovative visualizations to convey CAMS forecast uncertainties in time series and maps. The development is strongly user-driven and involves iterative consultation with a wide range of expert and non-expert users. We investigate the feasibility of different bivariate techniques to communicate the ensemble's best estimate and its uncertainty in a single map. We explore user preferences for a variety of time-series graphs, including boxplots, violinplots, and fancharts. Whilst preferences are largely driven by the data and visualization literacy of the users, we identify some generally valid best practices in terms of graph types, choices of colors and labels, and accompanying textual explanations. Finally, we present our candidate designs for the public display of air quality forecasts on the regional CAMS webpage.</p>


2018 ◽  
Vol 195 ◽  
pp. 04021
Author(s):  
Budiman Napitupulu ◽  
Ismiyati Ismiyati ◽  
Mudjiastuti Handajani

Traffic jams are a common phenomenon in Indonesia’s big cities due to the proliferation of private vehicles. The resulting air pollution directly threatens public health and urban quality of life. This research is to identify and analyze air pollution levels and forecast the potential to improve air quality by developing mass public transportation. This research employs the dust sampler method to measure the air pollution levels and calculate traffic volume, while the simulation method is used to analyze the data. The results show that the air pollution levels detected exceed the air quality thresholds established by regulations in the Decision by the Minister of Health and Environment. Traffic jam levels indicated by the Degree of Saturation (SD) were determined to be, on average, > 0.75. The simulation results show that by changing modes of transportation traffic jam levels decrease, ranging from 0.2201 to 0.291(DS), and air pollution decreases. Specifically, nitrogen oxides (NOx) are reduced by 48.19 %; sulfur dioxide (SO2) is reduced by 51.77 %; particulate matter (PM) is reduced by 29.86%; lead (Pb) is reduced by 52.22%; and carbon monoxide (CO) is reduced by 52.15%. This research suggests the mass public transportation planning should be implemented.


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