Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.5194/acp-18-6223-2018 |
Random forest meteorological normalisation models for Swiss PM10 trend analysis | |
Grange, Stuart K.1,2; Carslaw, David C.1,3; Lewis, Alastair C.1,4; Boleti, Eirini2,5; Hueglin, Christoph2 | |
2018-05-03 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS
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ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2018 |
卷号 | 18期号:9页码:6223-6239 |
文章类型 | Article |
语种 | 英语 |
国家 | England; Switzerland |
英文摘要 | Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between -0.09 and -1.16 mu g m(-3) yr(-1) with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at -0.77 mu g m(-3) yr(-1). Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000431367200007 |
WOS关键词 | AIR-QUALITY ; OZONE ; VARIABILITY ; SWITZERLAND |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/30845 |
专题 | 地球科学 |
作者单位 | 1.Univ York, Wolfson Atmospher Chem Labs, York YO10 5DD, N Yorkshire, England; 2.Empa Swiss Fed Labs Mat Sci & Technol, CH-8600 Dubendorf, Switzerland; 3.Ricardo Energy & Environm, Harwell OX11 0QR, Oxon, England; 4.Univ York, Nat Ctr Atmospher Sci, York YO10 5DD, N Yorkshire, England; 5.Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland |
推荐引用方式 GB/T 7714 | Grange, Stuart K.,Carslaw, David C.,Lewis, Alastair C.,et al. Random forest meteorological normalisation models for Swiss PM10 trend analysis[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2018,18(9):6223-6239. |
APA | Grange, Stuart K.,Carslaw, David C.,Lewis, Alastair C.,Boleti, Eirini,&Hueglin, Christoph.(2018).Random forest meteorological normalisation models for Swiss PM10 trend analysis.ATMOSPHERIC CHEMISTRY AND PHYSICS,18(9),6223-6239. |
MLA | Grange, Stuart K.,et al."Random forest meteorological normalisation models for Swiss PM10 trend analysis".ATMOSPHERIC CHEMISTRY AND PHYSICS 18.9(2018):6223-6239. |
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