Abstract
The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident’s outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In order to take the periodicity of pollutants into empirical air quality forecasting models, this study evaluates the temporal variations of air pollutants and develops a novel sequence to sequence model with weekly periodicity to forecast air quality. Two-year observation data from Shanghai roadside air quality monitoring stations are employed to support analyzing and modeling. The results conclude that the fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations show obvious daily and weekly variations, and the temporal patterns are nearly consistent with the periodicity of traffic flow in Shanghai. Compared with PM2.5, the CO concentrations are more affected by traffic variation. The proposed model outperforms the baseline model in terms of accuracy, and presents a higher linear consistency in PM2.5 prediction and lower errors in CO prediction. This study could assist environmental researchers to further improve the technologies for urban air quality forecasting, and serve as tools for supporting policymakers to implement related traffic management and emission control policies.
Highlights
Traffic emissions have been one of the major contributors to urban air pollution in many cities around the world [1,2], and can deteriorate ambient air quality on a wide range of spatial scales.Epidemiological studies indicate that long-term exposure to traffic-related air pollution could harm human health [3], lead to respiratory and cardiovascular diseases, and even increase mortality [4,5].Even short-term exposure to ambient particulate air pollution could greatly increase the risk of myocardial infarction [6]
If the periodicity consistent with traffic flow patterns is fully considered in modeling, the deep learning models could be more suitable for roadside air quality forecasting with a higher accuracy
We propose a novel sequence to sequence (Seq2Seq) model with weekly periodicity to predict the traffic-related PM2.5 and carbon monoxide (CO) concentrations
Summary
Traffic emissions have been one of the major contributors to urban air pollution in many cities around the world [1,2], and can deteriorate ambient air quality on a wide range of spatial scales. The input parameters of the deterministic models (e.g., emission inventory) commonly contain very limited information of pollution sources, lack spatial and temporal dependencies for some air pollutants [12], and present strong difficulty when being updated in time due to the high cost These disadvantages could notably affect the prediction performance of the deterministic models. If the periodicity consistent with traffic flow patterns is fully considered in modeling, the deep learning models could be more suitable for roadside air quality forecasting with a higher accuracy. It is necessary to develop an advanced deep learning model including periodic features of the time series To address this issue, we propose a novel sequence to sequence (Seq2Seq) model with weekly periodicity to predict the traffic-related PM2.5 and CO concentrations. The characterization of the two air pollutants could represent different temporal patterns of traffic emissions and assist in evaluating the prediction performance of the proposed model responding to varying pollutants [31]
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