Links between recent trends in airborne pollen concentration, meteorological parameters and air pollutants

Oduber, Fernanda; Calvo, Ana; Blanco-Alegre, C.; Castro, Amaya; Vega-Maray, Ana María; Valencia-Barrera, Rosa María; Fernández‐González, Delia; Fraile, Roberto

doi:10.1016/j.agrformet.2018.09.023

Cited by 53 publications

(32 citation statements)

References 72 publications

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“…With more and more air pollution, the relationship between the concentrations of bioaerosol (airborne pollen) and meteorological parameters had become more complex. SO 2 , O 3 and PM affect the flowering times of several plant species; for example, the flowering time of Faxious and Populus was related to the concentration of O 3 in atmospheric pollutants in León (Spain) [2]. In addition, it has been shown that the inhalation of particulate matter is negatively related to pollen concentration, and that inhaled particulate matter adheres to pollen, causing changes in pollen morphology and structure [3,4].…”

Section: Introductionmentioning

confidence: 99%

Relationship between airborne pollen assemblages and major meteorological parameters in Zhanjiang, South China

Chen

et al. 2020

PLoS ONE

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Pollen is an important component of bioaerosol and the distribution of pollen and its relationship with meteorological parameters can be analyzed to better prevent hay fever. Pollen assemblages can also provide basic data for analyzing the relationship between bioaerosol and PM. We collected 82 samples of airborne pollen using a TSP large flow pollen collector from June 1, 2015 to June 1, 2016, from central Zhanjiang city in South China. We also conducted a survey of the nearby vegetation at the same time, in order to characterize the major plant types and their flowering times. We then used data on daily temperature, relative humidity, precipitation, vapor pressure and wind speed from a meteorological station in the center of Zhanjiang City to assess the relationship between the distribution of airborne pollen and meteorological parameters. Our main findings and conclusions are as follows: (1) We identified 15 major pollen types, including Pinus, Castanopsis, Myrica, Euphorbiaceae, Compositae, Gramineae, Microlepia and Polypodiaceae. From the vegetation survey, we found that the pollen from these taxa represented more than 75% of local pollen, while the pollen of Podocarpus, Dacrydium and other regional pollen types represented less than 25%. (2) The pollen concentrations varied significantly in different seasons. The pollen concentrations were at a maximum in spring, consisting mainly of tree pollen; the pollen concentrations were at an intermediate level in autumn and winter, consisting mainly of herb pollen and fern spores; and the pollen concentrations in summer were the lowest, consisting mainly of fern spores. (3) Analysis of the relationship between airborne pollen concentrations and meteorological parameters showed that variations in the pollen concentrations were mainly affected by temperature and relative humidity. In addition, there were substantial differences in these relationships in different seasons. In spring, pollen concentrations were mainly affected by temperature; in summer, they were mainly affected by the direction of the maximum wind speed; in autumn, they were mainly affected by relative humidity and temperature; and in winter, they were mainly affected by relative humidity and wind speed. Temperature and relative humidity promote plant growth and flowering. Notably, the variable

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Section: Introductionmentioning

confidence: 99%

Relationship between airborne pollen assemblages and major meteorological parameters in Zhanjiang, South China

Chen

et al. 2020

PLoS ONE

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“…where x i denoted the actual output, y i denoted the predicted outputs and n was the total number of observations [4,20].…”

Section: Model Performance Evaluation Criteriamentioning

confidence: 99%

“…The main air pollutants in the atmosphere are carbon monoxide (CO), nitrogen oxides (NO x ), particulate matter (PM), sulphur dioxide (SO 2 ) and ozone (O 3 ) [4]. Exposure to air pollutants escalates the risk of contravention of respiratory diseases, such as asthma, respiratory infections and chronic obstructive pulmonary disease, in children and adults alike [5].…”

Section: Introductionmentioning

confidence: 99%

Comparative study on the effects of meteorological and pollutant parameters on ANN modelling for prediction of SO2

Unnikrishnan

Madhu

2019

SN Appl. Sci.

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Variations in meteorological parameters, different transportation mechanisms, complex reaction mechanisms and insufficient control measures have made control of ambient air pollution, a challenge. Prediction mechanisms for pollutant concentrations in advance have become necessary to regulate parameters within the acceptable limits. Artificial neural network (ANN) modelling can be used for predicting the concentrations of pollutants by establishing functional relationships between complex and nonlinear predictor variables and outputs. The application of time-series ANN models to predict air quality parameters was investigated by using six pollutant variables, five meteorological parameters and three time parameters to predict the concentration of SO 2. An industrial belt in the southern part of India was selected as the study area. Two years of input parameters and feed-forward back propagation algorithm were used to construct the ANN model. Input parameters were optimized using forward selection and backward elimination techniques. Mean squared error and coefficient of determination were used to evaluate the models. The developed model exhibited very promising performance evaluation characteristics with the best model resulting in MSE of 0.0115 and R 2 value of 0.8979. The hybrid ANN models employing input parameter optimization techniques resulted in better performance characteristic values than conventional models. Any model exhibited a minimum reduction in MSE by 9% and 3% improvement in correlation. Pollutant parameters were found to affect the ANN models when compared to the meteorological parameters. Predicted values compared to the National Ambient Air Quality Standards were as low as 55% of the maximum allowable concentration in ambient air.

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“…Extending the time interval of pollen dispersal may improve the effectiveness of pollination and hence seed production. However, such an increase might become problematic for human health, in that airborne pollen can contribute to air pollution and respiratory allergies (e.g., rhinitis, asthma, and skin rash), especially in urban and industrialized areas (Pawankar et al, 2013;Schmidt, 2016;Oduber et al, 2019). In developed countries, about 15-30% of the population is affected by pollen-induced allergies (Aas et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Temporal patterns of pollen shedding for longleaf pine (Pinus palustris) at the Escambia Experimental Forest in Alabama, USA

Chen¹,

Brockway²,

Guo³

2020

Dendrobiology

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Longleaf pine is an important tree species in the southeastern United States and studying the temporal patterns of pollen shedding is crucial to a better understanding of its phenology and seed production. In this study, field observation data on the timing of pollen shedding from 1958 to 2013 were analyzed with reference to local weather conditions. Our results indicated that the time of peak pollen shedding after January 1 (TPPS) ranged from 53 days (about February 22) to 95 days (around April 5). There was no significant trend of decreasing TPPS. The number of days with the maximum air temperature above 0 °C was close to the TPPS. The accumulated maximum daily air temperature for the TPPS approximated an average of 1,342 °C. The TPPS declined with an increase in the average air temperature during winters. The time of 80% accumulated pollen density (TAPD) varied from 5 to 32 days, with an average of 13 days. Taylor’s power-law was evident in the TAPD data, with the time group of 10–15 days having an interval time of 2 years. Winter weather was not correlated with the TAPD. These results provide new information concerning the pollen phenology for longleaf pine trees.

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Links between recent trends in airborne pollen concentration, meteorological parameters and air pollutants

Cited by 53 publications

References 72 publications

Relationship between airborne pollen assemblages and major meteorological parameters in Zhanjiang, South China

Relationship between airborne pollen assemblages and major meteorological parameters in Zhanjiang, South China

Comparative study on the effects of meteorological and pollutant parameters on ANN modelling for prediction of SO2

Temporal patterns of pollen shedding for longleaf pine (Pinus palustris) at the Escambia Experimental Forest in Alabama, USA

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