Air quality forecasting using arti ficial neural networks with real time dynamic error correction in highly polluted regions
Air quality forecasting using arti ficial neural networks with real time dynamic error correction in highly polluted regions
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DOI：
10.1016/j.scitotenv.2020.139454
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发表时间：
2020-09-15
影响因子：
9.8
通讯作者：
Batra, Sakshi
中科院分区：
文献类型：
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作者：
Agarwal, Shivang;Sharma, Sumit;Batra, Sakshi
空气污染是一个重要问题，特别是在世界各地的大城市。这些城市内部和周边地区都有排放源，根据当时的气象条件，这些排放源导致空气质量波动。短期空气质量预测不仅用于可能减轻即将到来的高空气污染事件，而且还用于计划减少居民的暴露。在这项研究中，使用人工神经网络（ANN）的模型已经开发出预测污染物浓度的PM10，PM2.5，NO2，和O-3的当前一天和随后的4天在一个高度污染的地区（32个不同的位置在德里）。该模型使用2018年的气象参数和每小时污染浓度数据进行训练，然后用于实时生成空气质量预报。它还配备了真实的时间校正（RTC），通过根据过去几天的模型性能动态调整预测来提高预测质量。没有RTC的模型表现不错，但与RTC的误差进一步减少预测值。该模型的实用性已得到实时证明，并在2018年全年和2019年独立进行了模型验证。该模型在几个评价指标上对所有污染物都表现出非常好的性能。各污染物的相关系数在0 ~ 4天的预报值之间变化，在0.79 ~ 0.88 ~ 0.49 ~ 0.68之间。在四天的预测中，臭氧的性能恶化程度最低。RTC的使用进一步提高了所有污染物的模型性能。
Air pollution is an important issue, especially in megacities across the world. There are emission sources within and also in the regions around these cities, which cause fluctuations in air quality based on prevailing meteorological conditions. Short term air quality forecasting is used not to just possibly mitigate forthcoming high air pollution episodes, but also to plan for reduced exposures of residents. In this study, a model using Artificial Neural Networks (ANN) has been developed to forecast pollutant concentration of PM10, PM2.5, NO2, and O-3 for the current day and subsequent 4 days in a highly polluted region (32 different locations in Delhi). The model has been trained using meteorological parameters and hourly pollution concentration data for the year 2018 and then used for generating air quality forecasts in real-time. It has also been equipped with Real Time Correction (RTC), to improve the quality of the forecasts by dynamically adjusting the forecasts based on the model performance during the past few days. The model without RTC performs decently, but with RTC the errors are further reduced in forecasted values. The utility of the model has been demonstrated in real-time and model validations were performed for the whole year of 2018 and also independently for 2019. The model shows very good performance for all the pollutants on several evaluation metrics. Coefficient of correlations for various pollutants varies from 0.790.88 to 0.490.68 between the Day0 to Day4 forecasts. Lowest deterioration of performance was observed for ozone over the four days of forecasts. Use of RTC further improves the model performance for all pollutants.