Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring,Environment International

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Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring
Environment International ( IF 10.3 ) Pub Date : 2021-04-15 , DOI: 10.1016/j.envint.2021.106569
Jules Kerckhoffs ₁ , Gerard Hoek ₁ , Ulrike Gehring ₁ , Roel Vermeulen ₂

Affiliation

Background

Large nation- and region-wide epidemiological studies have provided important insights into the health effects of long-term exposure to outdoor air pollution. Evidence from these studies for the long-term effects of ultrafine particles (UFP), however is lacking. Reason for this is the shortage of empirical UFP land use regression models spanning large geographical areas including cities with varying topographies, peri-urban and rural areas. The aim of this paper is to combine targeted mobile monitoring and long-term regional background monitoring to develop national UFP models.

Method

We used an electric car to monitor UFP concentrations in selected cities and towns across the Netherlands over a 14-month period in 2016–2017. Routes were monitored 3 times and concentrations were averaged per road segment. In addition, we used kriging maps based on regional background monitoring (20 sites; 3 × 2 weeks) over the same period to assess annual average regional background concentrations. All road segments were used to model spatial variation of UFP with three different land-use (regression) approaches: supervised stepwise regression, LASSO and random forest. For each approach, we also tested a deconvolution method, which segregates the average concentration at each road segment into a local and background signal. Model performance was evaluated with short-term (400 sites across the Netherlands; 3 × 30 minutes) and external longer-term measurements (42 sites in two major cities; 3 × 24 hours). We also compared predictions of all six models at 1000 random addresses spread over the country.

Results

We found similar predictive performance for the six models, with validation R² values from 0.25 to 0.35 for short-term measurements and 0.52 to 0.60 for longer-term external measurements. Models with and without deconvolution had similar predictive performance. All models based on the deconvolution method included a regional background kriging map as important predictor. Correlations between predictions at random addresses were high with Pearson correlations from 0.84 to 0.99. Models overestimated exposure at the short-term and long-term sites by about 20–30% in all cases, with small differences between regions and road types.

Conclusion

We developed robust nation-wide models for long-term UFP exposure combining mobile monitoring with long-term regional background monitoring. Minor differences in predictive performance between different algorithms were found, but the deconvolution approach is considered more physically realistic. The models will be applied in Dutch nation-wide health studies.

中文翻译：

基于移动监测的全国超细颗粒空间变化模型

背景

全国和地区范围的大型流行病学研究为长期暴露于室外空气污染的健康影响提供了重要的见识。然而，这些研究缺乏超细颗粒（UFP）长期作用的证据。原因是缺乏经验丰富的UFP土地使用回归模型，该模型跨越了包括地理条件各异的城市，郊区和农村地区在内的广大地理区域。本文的目的是将目标移动监视和长期区域背景监视相结合，以开发国家UFP模型。

方法

在2016-2017年的14个月内，我们使用电动汽车监测了整个荷兰特定城镇的UFP浓度。监控路线3次，并平均每个路段的浓度。此外，我们在同期使用基于区域背景监测（20个站点； 3×2周）的克里金图来评估年度平均区域背景浓度。所有路段均使用三种不同的土地利用（回归）方法对UFP的空间变化进行建模：监督逐步回归，LASSO和随机森林。对于每种方法，我们还测试了一种反卷积方法，该方法将每个路段的平均浓度分离为局部信号和背景信号。通过短期评估模型性能（荷兰境内有400个站点；3×30分钟）和外部长期测量（两个主要城市的42个站点； 3×24小时）。我们还比较了遍布全国的1000个随机地址下所有六个模型的预测。

结果

我们发现这六个模型具有相似的预测性能，短期测量的验证R ²值从0.25到0.35，长期外部测量的验证R ²值从0.52到0.60。带有和不带有反卷积的模型具有相似的预测性能。所有基于反卷积方法的模型都包含区域背景克里金图作为重要预测因子。随机地址的预测之间的相关性很高，皮尔森相关性从0.84到0.99。在所有情况下，模型都高估了短期和长期站点的暴露量约20％至30％，而地区和道路类型之间的差异很小。