.png){kind=icon}
返回
Improving 3-day deterministic air pollution forecasts using machine learning algorithms
.png){kind=icon}
OA
AI
DOI：10.5194/acp-24-807-2024.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As air pollution is regarded as the single largest environmental health risk in Europe it is important that communication to the public is up to date and accurate and provides means to avoid exposure to high air pollution levels. Long- and short-term exposure to outdoor air pollution is associated with increased risks of mortality and morbidity. Up-to-date information on present and coming days' air quality helps people avoid exposure during episodes with high levels of air pollution. Air quality forecasts can be based on deterministic dispersion modelling, but to be accurate this requires detailed information on future emissions, meteorological conditions and process-oriented dispersion modelling. In this paper, we apply different machine learning (ML) algorithms - random forest (RF), extreme gradient boosting (XGB), and long short-term memory (LSTM) - to improve 1, 2, and 3 d deterministic forecasts of PM 10 , NO x , and O 3 at different sites in Greater Stockholm, Sweden.It is shown that the deterministic forecasts can be significantly improved using the ML models but that the degree of improvement of the deterministic forecasts depends more on pollutant and site than on what ML algorithm is applied. Also, four feature importance methods, namely the mean decrease in impurity (MDI) method, permutation method, gradient-based method, and Shapley additive explanations (SHAP) method, are utilized to identify significant features that are common and robust across all models and methods for a pollutant. Deterministic forecasts of PM 10 are improved by the ML models through the input of lagged measurements and Julian day partly reflecting seasonal variations not properly parameterized in the deterministic forecasts. A systematic discrepancy by the deterministic forecasts in the diurnal cycle of NO x is removed by the ML models considering lagged measurements and calendar data like hour and weekday, reflecting the influence of local traffic emissions. For O 3 at the urban background site, the local photochemistry is not properly accounted for by the relatively coarse Copernicus Atmosphere Monitoring Service ensemble model (CAMS) used here for forecasting O 3 but is compensated for using the ML models by taking lagged measurements into account.Through multiple repetitions of the training process, the resulting ML models achieved improvements for all sites and pollutants. For NO x at street canyon sites, mean squared error (MSE) decreased by up to 60 %, and seven metrics, such as R 2 and mean absolute percentage error (MAPE), exhibited consistent results. The prediction of PM 10 is improved significantly at the urban background site, whereas the ML models at street sites have difficulty capturing more information. The prediction accuracy of O 3 also modestly increased, with differences between metrics.Further work is needed to reduce deviations between model results and measurements for short periods with relatively high concentrations (peaks) at the street canyon sites. Such peaks can be due to a combination of non-typical emissions and unfavourable meteorological conditions, which are rather difficult to forecast. Furthermore, we show that general models trained using data from selected street sites can improve the deterministic forecasts of NO x at the station not involved in model training. For PM 10 this was only possible using more complex LSTM models. An important aspect to consider when choosing ML algorithms is the computational requirements for training the models in the deployment of the system. Tree-based models (RF and XGB) require fewer computational resources and yield comparable performance in comparison to LSTM. Therefore, tree-based models are now implemented operationally in the forecasts of air pollution and health risks in Stockholm. Nevertheless, there is big potential to develop generic models using advanced ML to take into account not only local temporal variation but also spatial variation at different stations.
Keyword:
SURFACE MOISTURE MODEL
COUPLED ROAD DUST
EMISSIONS
QUALITY
IMPACT
PM10
期刊
IF:
5.1
论文数:
1.4W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions全球观测分析，以了解在极低的人为排放条件下空气质量的变化
OAAI
OAAI分享收藏
查看更多内容