title: A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function.
abstract: Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.
authors.affiliation: National Institute of Meteorological Sciences, Seogwipo-si, Republic of Korea. krk9@kma.go.kr.
authors.forename: Kyu Rang
authors.identifier: http://orcid.org/0000-0001-8872-6751
authors.initials: KR
authors.lastname: Kim
authors.affiliation: National Institute of Meteorological Sciences, Seogwipo-si, Republic of Korea.
authors.forename: Mijin
authors.initials: M
authors.lastname: Kim
authors.affiliation: National Institute of Meteorological Sciences, Seogwipo-si, Republic of Korea.
authors.forename: Ho-Seong
authors.initials: HS
authors.lastname: Choe
authors.affiliation: National Institute of Meteorological Sciences, Seogwipo-si, Republic of Korea.
authors.forename: Mae Ja
authors.initials: MJ
authors.lastname: Han
authors.affiliation: National Institute of Meteorological Sciences, Seogwipo-si, Republic of Korea.
authors.forename: Hye-Rim
authors.initials: HR
authors.lastname: Lee
authors.affiliation: College of Medicine, Hanyang University, Seoul, Republic of Korea.
authors.forename: Jae-Won
authors.initials: JW
authors.lastname: Oh
authors.affiliation: National Institute of Meteorological Sciences, Seogwipo-si, Republic of Korea.
authors.forename: Baek-Jo
authors.initials: BJ
authors.lastname: Kim
mesh_terms: D000393:Air Pollutants / Q000032:analysis; D000485:Allergens / Q000032:analysis*; D005544:Forecasting; D027582:Humulus; D008954:Models, Biological; D015233:Models, Statistical; D011058:Pollen; D029963:Quercus; D012044:Regression Analysis; D056910:Republic of Korea; D012306:Risk
chemical_list: D000393:Air Pollutants; D000485:Allergens
keywords: Allergy; Concentration; Pollen; Risk warning; Weibull PDF