Hjort Jan, Hugg Timo T, Antikainen Harri, Rusanen Jarmo, Sofiev Mikhail, Kukkonen Jaakko, Jaakkola Maritta S, Jaakkola Jouni J K
Geography Research Unit, and University of Oulu, Oulu, Finland.
Environ Health Perspect. 2016 May;124(5):619-26. doi: 10.1289/ehp.1509761. Epub 2015 Oct 9.
Despite the recent developments in physically and chemically based analysis of atmospheric particles, no models exist for resolving the spatial variability of pollen concentration at urban scale.
We developed a land use regression (LUR) approach for predicting spatial fine-scale allergenic pollen concentrations in the Helsinki metropolitan area, Finland, and evaluated the performance of the models against available empirical data.
We used grass pollen data monitored at 16 sites in an urban area during the peak pollen season and geospatial environmental data. The main statistical method was generalized linear model (GLM).
GLM-based LURs explained 79% of the spatial variation in the grass pollen data based on all samples, and 47% of the variation when samples from two sites with very high concentrations were excluded. In model evaluation, prediction errors ranged from 6% to 26% of the observed range of grass pollen concentrations. Our findings support the use of geospatial data-based statistical models to predict the spatial variation of allergenic grass pollen concentrations at intra-urban scales. A remote sensing-based vegetation index was the strongest predictor of pollen concentrations for exposure assessments at local scales.
The LUR approach provides new opportunities to estimate the relations between environmental determinants and allergenic pollen concentration in human-modified environments at fine spatial scales. This approach could potentially be applied to estimate retrospectively pollen concentrations to be used for long-term exposure assessments.
Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, Jaakkola MS, Jaakkola JJ. 2016. Fine-scale exposure to allergenic pollen in the urban environment: evaluation of land use regression approach. Environ Health Perspect 124:619-626; http://dx.doi.org/10.1289/ehp.1509761.
尽管近期在大气颗粒物的物理和化学分析方面取得了进展，但尚无模型可用于解析城市尺度上花粉浓度的空间变异性。
我们开发了一种土地利用回归（LUR）方法，用于预测芬兰赫尔辛基大都市区的空间细尺度致敏花粉浓度，并根据现有经验数据评估模型的性能。
我们使用了花粉高峰期在市区16个地点监测到的草花粉数据以及地理空间环境数据。主要统计方法为广义线性模型（GLM）。
基于GLM的LUR解释了所有样本草花粉数据中79%的空间变异，排除两个浓度极高地点的样本后，变异解释率为47%。在模型评估中，预测误差范围为观测到的草花粉浓度范围的6%至26%。我们的研究结果支持使用基于地理空间数据的统计模型来预测城市内部尺度上致敏草花粉浓度的空间变异。基于遥感的植被指数是局部尺度暴露评估中花粉浓度的最强预测因子。
LUR方法为在精细空间尺度上估计人类改造环境中环境决定因素与致敏花粉浓度之间的关系提供了新机会。这种方法有可能用于回顾性估计用于长期暴露评估的花粉浓度。
Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, Jaakkola MS, Jaakkola JJ. 2016. 城市环境中致敏花粉的细尺度暴露：土地利用回归方法的评估。《环境健康展望》124:619 - 626；http://dx.doi.org/10.1289/ehp.1509761 。
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