scholarly journals Forecasting of Corylus, Alnus, and Betula pollen concentration in the air in Poland

2018 ◽  
Author(s):  
Jakub Nowosad
Keyword(s):  
Predictive Models ◽  
Meteorological Data ◽  
Pollen Concentration ◽  
Pollen Counts ◽  
Betula Pollen ◽  
Random Forest Method ◽  
Single Location ◽  
Atmospheric Pollen ◽  
Temporal And Spatial ◽  
Concentration Levels

Understanding of the behavior of atmospheric pollen concentration, as well as developing predictive models, can greatly help allergic sufferers. The aims of this study were (i) to determine mean multi-year characteristics of temporal and space–time autocorrelation of the pollen counts of Corylus, Alnus, and Betula in Poland, (ii) to create and evaluate Corylus, Alnus, and Betula pollen concentration levels predictions based on previous pollen count values from given sites, and (ii) to develop spatiotemporal predictive models of Corylus, Alnus, and Betula pollen concentration levels, using preprocessed gridded meteorological data. The monitoring of the concentrations of Corylus, Alnus, and Betula pollen in the air was conducted in 11 cities in Poland. Additionally, AGRI4CAST Interpolated Meteorological Data were used as predictor variables. The autocorrelation and cross-correlation functions were used to investigate temporal and spatial patterns. Random forest method was used to predict the high pollen concentration level of Corylus, Alnus, and Betula. The study provided an understanding of the temporal and spatiotemporal autocorrelation of Corylus, Alnus, and Betula pollen counts. The final models also proved to be capable of pollen levels predicting in continuous areas rather than in a single location.

scholarly journals EXAMINING THE COMPATIBILITY OF AIRCRAFT MOISTURE OBSERVATIONS AND OPERATIONAL RADIOSONDES

2021 ◽  
Author(s):  
Skylar S. Williams ◽  
Timothy J. Wagner ◽  
Ralph A. Petersen
Keyword(s):  
Spatial Data ◽  
Meteorological Data ◽  
Spatial Density ◽  
High Confidence ◽  
Sensing System ◽  
Vapor Sensing ◽  
Single Location ◽  
Data Gaps ◽  
Terrain Following ◽  
Temporal And Spatial

AbstractThe addition of moisture observations via the Water Vapor Sensing System (WVSS) from about 150 aircraft available operationally through the World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) program now provides highly reliable thermodynamic profiles of the troposphere. The nearly 900 profiles available daily provide greater temporal and spatial density than the operational radiosonde network over many parts of the U.S. Previous studies comparing WVSS reports with specially co-located radiosondes have documented the quality and consistency of the WVSS observations. These studies, however, have been limited for short periods at a single location. This study expands on the earlier evaluations by using operational U.S. radiosondes from 2015 in a variety of locations, seasons, and climates. Comparison profiles at radiosonde sites were calculated in pressure layers and then interpolated to terrain-following sigma coordinates to account for the differences in elevations of comparison sites and provide a better means of integrating the higher vertical resolution of AMDAR observations taken in the boundary layer. Overall, systematic differences between the WVSS and radiosondes are smallest just above the surface, with the WVSS observations being slightly moister than the radiosondes aloft, with WVSS reports being moister during ascent than descent - possibly the result of small hysteresis effects. Standard deviations averaged 1.3 g kg-1 near the surface over the year-long period. Differences varied by season and region. Overall, the results indicate that WVSS observations are compatible with radiosonde reports and can be used with high confidence to fill temporal and spatial data gaps.

scholarly journals Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count

Aerobiologia ◽  
2015 ◽  
Vol 32 (3) ◽  
pp. 453-468 ◽  
Author(s):  
Jakub Nowosad ◽  
Alfred Stach ◽  
Idalia Kasprzyk ◽  
Elżbieta Weryszko-Chmielewska ◽  
Krystyna Piotrowska-Weryszko ◽  
...  
Keyword(s):  
Forecasting Model ◽  
Pollen Count ◽  
Pollen Concentration ◽  
Spatiotemporal Correlation ◽  
Betula Pollen ◽  
Correlation Properties ◽  
Concentration Levels

scholarly journals Analysis of Ozone Pollution Characteristics and Influencing Factors in Northeast Economic Cooperation Region, China

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 843
Author(s):  
Jiaqi Tian ◽  
Chunsheng Fang ◽  
Jiaxin Qiu ◽  
Ju Wang
Keyword(s):  
Wind Speed ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Economic Cooperation ◽  
Ozone Pollution ◽  
Temporal And Spatial Variation ◽  
Temporal And Spatial ◽  
Pm2.5 And Pm10 ◽  
Peak Pattern ◽  
Pm2.5 Concentration

The increase in tropospheric ozone (O3) concentration has become one of the factors restricting urban development. This paper selected the important economic cooperation areas in Northeast China as the research object and collected the hourly monitoring data of pollutants and meteorological data in 11 cities from 1 January 2015 to 31 December 2019. The temporal and spatial variation trend of O3 concentration and the effects of meteorological factors and other pollutants, including CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and PM2.5 and PM10 (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) on ozone concentration were analyzed. At the same time, the variation period of O3 concentration was further analyzed by Morlet wavelet analysis. The results showed that the O3 pollution in the study area had a significant spatial correlation. The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast. Seasonally, the O3 concentration was the highest in spring, followed by summer, and the lowest in winter. The diurnal variation of O3 concentration presented a “single peak” pattern. O3 concentration had a significant positive correlation with temperature, sunshine duration, and wind speed and a significant anticorrelation with CO, NO2, SO2, and PM2.5 concentration. Under the time scale of a = 9, 23, O3 had significant periodic fluctuation, which was similar to those of wind speed and temperature.

scholarly journals Spatial Analysis of Urban Thermal Island in Northern Iran (Case Study: Rasht City)

2022 ◽  
Author(s):  
Ali Mohammadpourzeid ◽  
Bohloul Alijani ◽  
Mehry Akbary ◽  
Parviz Zeaieanfirouzabadi
Keyword(s):  
Land Surface ◽  
Meteorological Data ◽  
Land Use Changes ◽  
Negative Effects ◽  
Northern Iran ◽  
Spatial Trend ◽  
Two Factors ◽  
Temporal And Spatial ◽  
Landsat Satellite ◽  
The City

Abstract Land surface temperature (LST) is one of the key parameters in hydrology, meteorology, and the surface energy balance.The one-window algorithm of Kim et al. Uses Landsat satellite imagery to model the earth's surface temperature.These trends are validated using meteorological data. Two main and basic factors play a major role in the temporal and spatial trend of the thermal islands of Rasht. These two factors of climate change that have occurred in the last two decades in the region of Gilan province and the city of Rasht. The second factor that has greatly enhanced the effect of the first factor is the human factor that has greatly included other urban factors in Rasht, including urban management and proper urban planning in the province and the city of Rasht. These two factors in the temporal and spatial trend of urban thermal islands have caused thermal islands to rapidly increase the growth of the city and urban population from the urban center to the western and southwestern regions and have very negative effects on land use changes and human areas. It has caused the construction of Rasht city.

Prediction of transcription factors binding events based on epigenetic modifications in different human cells

Epigenomics ◽  
2020 ◽  
Vol 12 (16) ◽  
pp. 1443-1456
Author(s):  
Yan Huang ◽  
Dianshuang Zhou ◽  
Yihan Wang ◽  
Xingda Zhang ◽  
Mu Su ◽  
...  
Keyword(s):  
Cell Lines ◽  
Predictive Models ◽  
High Performance ◽  
Epigenetic Modifications ◽  
The Cancer Genome Atlas ◽  
Encode Project ◽  
Atlas Data ◽  
Random Forest Method ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: We aim to predict transcription factor (TF) binding events from knowledge of gene expression and epigenetic modifications. Materials & methods: TF-binding events based on the Encode project and The Cancer Genome Atlas data were analyzed by the random forest method. Results: We showed the high performance of TF-binding predictive models in GM12878, HeLa, HepG2 and K562 cell lines and applied them to other cell lines and tissues. The genes bound by the top TFs ( MAX and MAZ) were significantly associated with cancer-related processes such as cell proliferation and DNA repair. Conclusion: We successfully constructed TF-binding predictive models in cell lines and applied them in tissues.

scholarly journals Prediction of Botrytis cinerea Risk in Vineyards Based on Weather Indicators

2020 ◽  
Vol 77 (2) ◽  
pp. 58
Author(s):  
Liliana PIRCALABU ◽  
Elena BRINDUSE ◽  
Marian ION
Keyword(s):  
High Risk ◽  
Botrytis Cinerea ◽  
Predictive Models ◽  
Meteorological Data ◽  
Grey Mould ◽  
Low Risk ◽  
Wine Quality ◽  
The Hours ◽  
Medium Risk ◽  
Positive Correlations

Botrytis cinerea causes grey mould, a major disease occurring in vineyards worldwide, resulting in loss of grape production and wine quality. Predictive models of favorability of Botrytis cinerea were used. Therefore, a series of meteorological data from 2010 to 2019 was used. The results showed that the frequency of years with low risk of Botrytis cinerea was 10%, medium risk 10%, high risk 80%. The disease can drastically reduce both yield and wine quality (Ribereau Gayon et al., 1980). The harvest years 2010, 2011, 2012, 2013 and 2014, favored the manifestation of an attack degree of 62.9% (2012) and 34.2% (2013). Positive correlations were observed in the case of Broome index and Bacchus index with the duration of sunlight (r2 = 0.935), respectively (r2 = 0.944) and the sum of the hours of moisture on the leaves (r2 = 0.833, r2 = 0.848). Based on the results a model for prediction of Botrytis cinerea risk will be developed.

scholarly journals Use of linear regression models to determine influence factors on the concentration levels of radon in occupied houses

2016 ◽  
Vol 44 ◽  
pp. 1660223
Author(s):  
Jonas Buermeyer ◽  
Matthias Gundlach ◽  
Anna-Lisa Grund ◽  
Volker Grimm ◽  
Alexander Spizyn ◽  
...  
Keyword(s):  
Linear Regression ◽  
Radon Concentration ◽  
Meteorological Data ◽  
Influence Factors ◽  
Environmental Parameters ◽  
Linear Regression Models ◽  
Indoor Climate ◽  
Additional Influence ◽  
Radon Measurements ◽  
Concentration Levels

This work is part of the analysis of the effects of constructional energy-saving measures to radon concentration levels in dwellings performed on behalf of the German Federal Office for Radiation Protection. In parallel to radon measurements for five buildings, both meteorological data outside the buildings and the indoor climate factors were recorded. In order to access effects of inhabited buildings, the amount of carbon dioxide (CO[Formula: see text] was measured. For a statistical linear regression model, the data of one object was chosen as an example. Three dummy variables were extracted from the process of the CO2 concentration to provide information on the usage and ventilation of the room. The analysis revealed a highly autoregressive model for the radon concentration with additional influence by the natural environmental factors. The autoregression implies a strong dependency on a radon source since it reflects a backward dependency in time. At this point of the investigation, it cannot be determined whether the influence by outside factors affects the source of radon or the habitant’s ventilation behavior resulting in variation of the occurring concentration levels. In any case, the regression analysis might provide further information that would help to distinguish these effects. In the next step, the influence factors will be weighted according to their impact on the concentration levels. This might lead to a model that enables the prediction of radon concentration levels based on the measurement of CO2 in combination with environmental parameters, as well as the development of advices for ventilation.

scholarly journals Seasonal variation in diurnal atmospheric grass pollen concentration profiles

2013 ◽  
Vol 10 (9) ◽  
pp. 14627-14656 ◽  
Author(s):  
R. G. Peel ◽  
P. V. Ørby ◽  
C. A. Skjøth ◽  
R. Kennedy ◽  
V. Schlünssen ◽  
...  
Keyword(s):  
Grass Pollen ◽  
Pollen Season ◽  
Meteorological Factors ◽  
Time Of Day ◽  
Grass Species ◽  
Pollen Concentration ◽  
Late Season ◽  
Pollen Loads ◽  
Pollen Concentrations ◽  
Atmospheric Pollen

Abstract. In this study, the diurnal atmospheric grass pollen concentration profile within the Danish city of Aarhus was shown to change in a systematic manner as the season progressed. Although diurnal grass pollen profiles can differ greatly from day-to-day, it is common practice to establish the time of day when peak concentrations are most likely to occur using seasonally-averaged diurnal profiles. Atmospheric pollen loads are highly dependent upon emissions, and different species of grass are known to flower and emit pollen at different times of the day and during different periods of the pollen season. Pollen concentrations are also influenced by meteorological factors – directly through those parameters that govern pollen dispersion and transport, and indirectly through the weather-driven flowering process. We found that three different profiles dominated the grass pollen season in Aarhus – a twin peak profile in the early season, a single evening profile in the mid-season, and a single midday peak in the late season. Whilst this variation could not be explained by meteorological factors, no inconsistencies were found with the theory that the variation was driven by a succession of different grass species with different diurnal flowering patterns dominating atmospheric pollen loads as the season progressed. The potential for exposure was found to be significantly greater during the late season period than during either the early or mid season periods.

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