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首页> 外文期刊>Nitrogen >Application of Density Plots and Time Series Modelling to the Analysis of Nitrogen Dioxides Measured by Low-Cost and Reference Sensors in Urban Areas
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Application of Density Plots and Time Series Modelling to the Analysis of Nitrogen Dioxides Measured by Low-Cost and Reference Sensors in Urban Areas

机译：密度图和时间序列建模在城市地区低成本和参考传感器测量的氮二氧化氮分析

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Temporal variability of NO2 concentrations measured by 28 Envirowatch E-MOTEs, 13 AQMesh pods, and eight reference sensors (five run by Sheffield City Council and three run by the Department for Environment, Food and Rural Affairs (DEFRA)) was analysed at different time scales (e.g., annual, weekly and diurnal cycles). Density plots and time variation plots were used to compare the distributions and temporal variability of NO2 concentrations. Long-term trends, both adjusted and non-adjusted, showed significant reductions in NO2 concentrations. At the Tinsley site, the non-adjusted trend was ?0.94 (?1.12, ?0.78) μgm?3/year, whereas the adjusted trend was ?0.95 (?1.04, ?0.86) μgm?3/year. At Devonshire Green, the non-adjusted trend was ?1.21 (?1.91, ?0.41) μgm?3/year and the adjusted trend was ?1.26 (?1.57, ?0.83) μgm?3/year. Furthermore, NO2 concentrations were analysed employing univariate linear and nonlinear time series models and their performance was compared with a more advanced time series model using two exogenous variables (NO and O3). For this purpose, time series data of NO, O3 and NO2 were obtained from a reference site in Sheffield, which were more accurate than the measurements from low-cost sensors and, therefore, more suitable for training and testing the model. In this article, the three main steps used for model development are discussed: (i) model specification for choosing appropriate values for p, d and q, (ii) model fitting (parameters estimation), and (iii) model diagnostic (testing the goodness of fit). The linear auto-regressive integrated moving average (ARIMA) performed better than the nonlinear counterpart; however, its performance in predicting NO2 concentration was inferior to ARIMA with exogenous variables (ARIMAX). Using cross-validation ARIMAX demonstrated strong association with the measured concentrations, with a correlation coefficient of 0.84 and RMSE of 9.90. ARIMAX can be used as an early warning tool for predicting potential pollution episodes in order to be proactive in adopting precautionary measures.
机译：在不同的时间内分析了28个Envirowatch E-Metes，13个AQMESH豆荚和八个参考传感器的NO2浓度的时间可变性鳞片（例如，年度，每周和昼夜循环）。密度图和时间变化地块用于比较NO2浓度的分布和时间变异性。调整和未调整的长期趋势表现出NO2浓度的显着减少。在Tinsley网站上，非调整趋势是？0.94（？1.12，？0.78）μgm？3 /年，而调整后的趋势是？0.95（？1.04，？0.86）μgm？3 /年。在德蒙斯郡绿色，不调整的趋势是？1.21（？1.91，？0.41）μgm？3 /年和调整后的趋势是？1.26（？1.57，？0.83）μgm？3 /年。此外，使用单变量线性和非线性时间序列模型分析NO2浓度，并使用两个外源变量（NO和O3）与更高级的时间序列模型进行比较它们的性能。为此目的，NO，O3和NO2的时间序列数据是从谢菲尔德的参考现场获得的，比低成本传感器的测量更准确，因此更适合训练和测试模型。在本文中，讨论了用于模型开发的三个主要步骤：（i）用于选择P，D和Q，（II）模型拟合（参数估计）和（iii）模型诊断的适当值的模型规范（测试适合的善意）。线性自动回归集成的移动平均（Arima）比非线性对应物更好;然而，其在预测NO 2浓度方面的性能与外源变量（ARIMAX）差不多。使用交叉验证ARIMAX与测量的浓度表现出强烈的关系，相关系数为0.84和9.90的RMSE。 ARIMAX可用作预测潜在污染事件的预警工具，以便积极采用预防措施。

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