Abstract Land use regression (LUR) modeling has been applied to study the spatiotemporal patterns of air pollution, which when combined with human space-time activity, is important in understanding the health effects of air pollution. However, most of these studies focus either on the temporal or the spatial domain and do not consider the variability in both space and time. A temporally aggregated model does not reflect the temporal variability caused by traffic and atmospheric conditions and leads to inaccurate estimation of personal exposure. Besides, most studies focus on a single air pollutant (e.g., O3, NO2, or NO). These pollutants have a strong interaction due to photochemical processes. For studying relations between spatial and temporal patterns in these pollutants it is preferable to use a uniform data source and modelling approach which makes comparison of pollution surfaces between pollutants more reliable as they are produced with the same methodology. We developed temporal land use regression models of O3, NO2 and NO to study the co-variability of these pollutants and the relations with typical weather conditions over the year. We use hourly concentrations from the measurement network of the Dutch National Institute for Public Health and the Environment and aggregate them by hour, for weekday/weekend and month, and fit a regression model for each hour of the day. 70 candidate predictors that are known to have a strong relationship with combustion-related emissions are evaluated in the LUR modelling process. For all pollutants, the optimal LUR was identified with 4 predictors and the temporal variability was determined by the explained variance of each temporal model. Our temporal models for O3, NO2, and NO strongly reflect the photochemical processes in space and time. O3 shows a high background value throughout the day and only dips in the (close) vicinity of roads. The diminishing rate is affected by traffic intensity. The NO2 LUR is validated against NO2 measurements from the Traffic-Related Air pollution and Children's respiratory HEalth and Allergies (TRACHEA) study, resulting in an R2 of 0.61.

中文翻译：

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土地利用回归模型揭示了荷兰 NO2、NO 和 O3 的时空协变

摘要 土地利用回归 (LUR) 模型已被应用于研究空气污染的时空模式，将其与人类时空活动相结合，对于了解空气污染对健康的影响非常重要。然而，这些研究大多集中在时间或空间领域，没有考虑空间和时间的可变性。时间聚合模型不能反映由交通和大气条件引起的时间变化，并导致对个人暴露的估计不准确。此外，大多数研究集中于单一的空气污染物（例如，O3、NO2 或 NO）。由于光化学过程，这些污染物具有很强的相互作用。为了研究这些污染物的空间和时间模式之间的关系，最好使用统一的数据源和建模方法，这使得污染物之间的污染面比较更加可靠，因为它们是用相同的方法产生的。我们开发了 O3、NO2 和 NO 的时间土地利用回归模型来研究这些污染物的协变以及与一年中典型天气条件的关系。我们使用来自荷兰国家公共卫生与环境研究所测量网络的每小时浓度，并按小时、工作日/周末和月份汇总它们，并为一天中的每个小时拟合回归模型。在 LUR 建模过程中评估了 70 个已知与燃烧相关排放有密切关系的候选预测因子。对于所有污染物，最佳 LUR 由 4 个预测变量确定，时间变异性由每个时间模型的解释方差确定。我们的 O3、NO2 和 NO 时间模型强烈反映了空间和时间的光化学过程。O3 全天都显示出很高的背景值，并且仅在道路（靠近）附近下降。递减率受交通强度影响。NO2 LUR 已根据交通相关空气污染和儿童呼吸道健康与过敏 (TRACHEA) 研究中的 NO2 测量值进行验证，结果 R2 为 0.61。O3 全天都显示出很高的背景值，并且仅在道路（靠近）附近下降。递减率受交通强度影响。NO2 LUR 已根据交通相关空气污染和儿童呼吸道健康与过敏 (TRACHEA) 研究中的 NO2 测量值进行验证，结果 R2 为 0.61。O3 全天都显示出很高的背景值，并且仅在道路（靠近）附近下降。递减率受交通强度影响。NO2 LUR 已根据交通相关空气污染和儿童呼吸道健康和过敏 (TRACHEA) 研究中的 NO2 测量值进行验证，结果 R2 为 0.61。