Pollution by Urticaceae pollen—influence of selected air pollutants and meteorological parameters

2016 ◽  
Vol 23 (10) ◽  
pp. 10072-10079 ◽  
Author(s):  
Nataša Čamprag Sabo ◽  
Tibor Kiš ◽  
Peđa Janaćković ◽  
Dragana Đorđević ◽  
Aleksandar Popović
Keyword(s):  
Air Pollutants ◽  
Meteorological Parameters ◽  
Urticaceae Pollen

Changes of air pollutants’ concentrations in selected Romanian cities during the pandemic year 2020

2021 ◽  
Author(s):  
Gabriela Iorga ◽  
George-Bogdan Burghelea
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
European Commission ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Dust Particles ◽  
Spatial And Temporal Variation ◽  
Pollution Level ◽  
Data Set ◽  
Air Mass

<p>Present research contributes to scientific knowledge concerning spatial and temporal variation of major air pollutants with high resolution at the country scale bringing statistical information on concentrations of NOx, O<sub>3</sub>, CO, SO<sub>2</sub> and particulate matter with an aerodynamic diameter below 10 μm (PM<sub>10</sub>) and below 2.5 μm (PM<sub>2.5</sub>) during the pandemic year 2020 using an observational data set from the Romanian National Air Quality Network in seven selected cities spread out over the country. These cities have different level of development, play regional roles, might have potential influence at European scale and they are expected to be impacted by different pollution sources. Among them, three cities (Bucharest, Brașov, Iași) appear frequently on the list of the European Commission with reference to the infringement procedure that the European Commission launched against Romania in the period 2007-2020 regarding air quality.</p><p>Air pollutant data was complemented with local meteorological parameters at each site (atmospheric pressure, relative humidity, temperature, global solar radiation, wind speed and direction). Statistics of air pollutants provide us with an overview of air pollution in main Romanian cities.  Correlations between meteorological parameters and ambient pollutant levels were analyzed. Lowest air pollution levels were measured during the lockdown period in spring, as main traffic and non-essential activities were severely restricted. Among exceptions were the construction activities that were not interrupted. During 2020, some of selected cities experienced few pollution episodes which were due to dust transport from Sahara desert. However, in Bucharest metropolitan area, some cases with high pollution level were found correlated with local anthropogenic activity namely, waste incinerations. Air mass origins were investigated for 72 hours back by computing the air mass backward trajectories using the HYSPLIT model. Dust load and spatial distribution of the aerosol optical depth with BSC-DREAM8b v2.0 and NMBM/BSC-Dust models showed the area with dust particles transport during the dust events.</p><p>The obtained results are important for investigations of sources of air pollution and for modeling of air quality.</p><p><strong> </strong></p><p><strong>Acknowledgment:</strong></p><p>The research leading to these results has received funding from the NO Grants 2014-2021, under Project contract no. 31/2020, EEA-RO-NO-2019-0423 project. NOAA Air Resources Laboratory for HYSPLIT transport model, available at READY website https://www.ready.noaa.gov  and the Barcelona dust forecast center for BSC-DREAM8b and NMBM/BSC-Dust models, available at:  https://ess.bsc.es/bsc-dust-daily-forecast are also acknowledged. The data regarding ground-based air pollution and meteorology by site was extracted from the public available Romanian National Air Quality Database, www.calitateaer.ro.</p>

Correlation analysis of gaseous air pollutants and meteorological parameters in naturally ventilated dairy farm

2020 ◽  
Vol 19 (4) ◽  
pp. 353-361
Author(s):  
SungHak Lee ◽  
HyeSeung Kim ◽  
HungSoo Joo
Keyword(s):  
Correlation Analysis ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Dairy Farm

scholarly journals Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China

2020 ◽  
Vol 204 ◽  
pp. 111035 ◽  
Author(s):  
Shaowei Lin ◽  
Donghong Wei ◽  
Yi Sun ◽  
Kun Chen ◽  
Le Yang ◽  
...  
Keyword(s):  
Air Pollutants ◽  
Meteorological Parameters ◽  
Mainland China

Exploratory analysis of the atmospheric levels of BTEX, criteria air pollutants and meteorological parameters in a tropical urban area in Northeastern Brazil

2020 ◽  
Vol 152 ◽  
pp. 104265 ◽  
Author(s):  
Lícia P.S. Cruz ◽  
Daniela F. Santos ◽  
Ivanice F. dos Santos ◽  
Ícaro V.S. Gomes ◽  
Akácia V.S. Santos ◽  
...  
Keyword(s):  
Urban Area ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Exploratory Analysis ◽  
Northeastern Brazil ◽  
Criteria Air Pollutants

scholarly journals Seasonal and Temporal Variations of Criteria Air Pollutants and the Influence of Meteorological Parameters on the Concentration of Pollutants in Ambient Air in Lahore, Pakistan

2016 ◽  
Vol 59 (1) ◽  
pp. 34-42
Author(s):  
Amtul Bari Tabinda ◽  
Saleha Munir ◽  
Abdullah Yasar ◽  
Asad Ilyas
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Negative Correlation ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Ambient Air ◽  
Temporal Variations ◽  
Criteria Air Pollutants ◽  
Summer Minimum ◽  
One Year

Criteria air pollutants have their significance for causing health threats and damage to theenvironment. The study was conducted to assess the seasonal and temporal variations of criteria air pollutantsand evaluating the correlations of criteria air pollutants with meteorological parameters in the city ofLahore, Pakistan for a period of one year from April 2010 to March 2011. The concentrations of criteriaair pollutants were determined at fixed monitoring stations equipped with HORIBA analyzers. The annualaverage concentrations (µg/m3) of PM2.5, O3, SO2, CO and NOx (NO+NO2) for this study period were118.94±57.46, 46.0±24.2, 39.9±8.9, 1940±1300 and 130.9±81.0 (61.8±46.2+57.3±22.19), respectively.PM2.5, SO2, CO and NOx had maximum concentrations during winter whereas O3 had maximum concentrationduring summer. Minimum concentrations of PM2.5, SO2 and NOx were found during monsoon as comparedto other seasons due to rainfall which scavenged these pollutants. The O3 showed positive correlation withtemperature and solar radiation but negative correlation with wind speed. All other criteria air pollutantsshowed negative correlation with wind speed, temperature and solar radiation. A significant (P<0.01)correlation was found between NOx and CO (r = 0.779) which showed that NOx and CO arise from commonsource that could be the vehicular emission. PM2.5 was significantly correlated (P<0.01) with NOx (r = 0.524)and CO (r = 0.519), respectively. High traffic intensity and traffic jams were responsible for increased airpollutants level especially the PM2.5, NOx and CO.

scholarly journals A Survey On Air Quality Prediction Using Traditional Statistics Method

2020 ◽  
pp. 942-946
Author(s):  
S. Karthikeyani ◽  
S. Rathi
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Moving Average ◽  
Quality Prediction ◽  
Atmospheric Air ◽  
Air Quality Prediction ◽  
Naive Method ◽  
Auto Regressive ◽  
Weighted Moving Average

Air pollution is the release of pollutants into the atmospheric air which are harmful to human health and the planet as a whole. Car emissions, dust, pollen, chemicals from factories and mold spores may be suspended as a particle. In this survey, the analyzes are made revolving on air quality prediction using the traditional statistics method. The prediction using air pollutants are PM2.5, PM10, NO2, NOx, NO, SO2, CO, O3 and meteorological parameters such as Absolute Temparathure(AT) and Relative Humidity(RH). In this comparison experiments, common predicted algorithms are Naive Method, Auto-Regressive Integrated Moving Average(ARIMA), Exponentially Weighted Moving Average(EWMA), Linear Regression(LR), LSTM model, Prophet Model are analyzed.

scholarly journals Influences of meteorological parameters and biological and chemical air pollutants on the incidence of asthma and rhinitis

2008 ◽  
Vol 37 ◽  
pp. 99-119 ◽  
Author(s):  
L Makra ◽  
S Tombácz ◽  
B Bálint ◽  
Z Sümeghy ◽  
T Sánta ◽  
...  
Keyword(s):  
Air Pollutants ◽  
Meteorological Parameters

scholarly journals The Transfer Phenomena of Air Pollutants Emitted from Power Station and Generators in Nasiriyah City and the Effect of Meteorological Parameters on its Dispersion by Using Fixed Box Model

2022 ◽  
Vol 961 (1) ◽  
pp. 012001
Author(s):  
Ahmed Alaa Hussein ◽  
Zahraa S. Mahdi ◽  
Nagam Obaid Kariem
Keyword(s):  
Wind Speed ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Emission Rate ◽  
Electrical Energy ◽  
Fuel Oil ◽  
Box Model ◽  
Upstream Direction ◽  
Light Oil ◽  
The City

Abstract The study aims to use the fixed box model to calculate the spread of pollutants (CO2, SO2, NOX, particulate) resulting from the burning of fuel used to produce electrical energy in the Nasiriyah city and to know the way they spread in the city through being affected by the wind speed and compare the results calculated from the model with the results measured by the lancom4 device. The results showed that the main pollutants for the air in Nasiriyah was emitted from burning the fuel used for the production of electric power, and the results showed that the concentration of pollutants (CO2, SO2, NOX) was much higher inside the city when compared with the upstream direction of the winds due to its increase with the movement of winds and its entry into the city. Through the application of the fixed box model and when comparing the calculated results through the model with the results measured by the lancom4 device, the error rate was (4 %, 2%, 2%, 5%) for pollutants (CO2, SO2, NOX, particulate) respectively, it was also observed that the highest emission rate of pollutants was result from using heavy fuel (fuel oil) and the lowest emission was from light oil (Dry gas). We noted the spread of pollutants and dilution in the atmosphere increases with the increase in wind speed, excluding for particles mater.

Air Pollutants and Meteorological Parameters Influence on PM2.5 Forecasting and Performance Assessment of the Developed Artificial Intelligence-Based Forecasting Model

2017 ◽  
Vol 68 (4) ◽  
pp. 864-868
Author(s):  
Marian Popescu ◽  
Sanda Florentina Mihalache ◽  
Mihaela Oprea
Keyword(s):  
Artificial Intelligence ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Fuzzy Inference ◽  
Computational Effort ◽  
Forecasting Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
And Performance ◽  
The Comparative Study

Particulate matter with an aerodynamic diameter lower than 2.5 �m (PM2.5) is one of the most important air pollutants. Current regulations impose measuring and limiting its concentrations. Thus, it is necessary to develop forecasting models programs that can inform the population about possible pollution episodes. This paper emphasizes the correlations between PM2.5 and other pollutants, and meteorological parameters. From these, nitrogen dioxide and temperature showed have the best correlations with PM2.5 and have been selected as inputs for the proposed forecasting model besides four PM2.5 concentrations (the values from current hour to three hours ago), the output of the model being the prediction of the next hour PM2.5 concentration. Two methods from artificial intelligence were used to build the forecasting model, namely adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN). The comparative study between these methods showed that the model which uses ANN have better results in terms of statistical indicators and computational effort.

Prediction of airborne pollen concentrations by artificial neural network and their relationship with meteorological parameters and air pollutants

2022 ◽  
Author(s):  
Gholamreza Goudarzi ◽  
Yaser Tahmasebi Birgani ◽  
Mohammad-Ali Assarehzadegan ◽  
Abdolkazem Neisi ◽  
Maryam Dastoorpoor ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Airborne Pollen ◽  
Pollen Concentrations ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document