Imputation methods for addressing missing data in short-term monitoring of air pollutants

Hadeed, Steven J; O'rourke, M. K.; Burgess, Jefferey L.; Harris, Robin B.; Canales, Robert A.

doi:10.1289/isee.2020.virtual.p-1015

Cited by 3 publications

(3 citation statements)

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“…50 Here we replace the missing data in all EPA sites, and PA sensors using the ARIMA model with Kalman filter. [51][52][53][54] The power spectral density of each EPA and PA hourly time series of PM 2.5 data was then calculated using the stats package in R.…”

Section: Spectral Analysis: Methodsmentioning

confidence: 99%

Understanding the source components captured by the Purple Air Network

Kumar

Senarathna

Gurajala

et al. 2023

Preprint

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PM2.5 has been linked to numerous pollution-mediated adverse health effects and their monitoring is key for taking preventative and mitigative measures. Accurate measurements of PM2.5 concentrations are available at EPA sites, but such data lacks spatial resolution due to a limited number of monitoring locations. In recent years the deployment of low-cost sensor networks has opened up the possibility of acquiring air quality data at a high spatiotemporal resolution. However, the sensitivity, noise, and accuracy of data acquired by low-cost sensors remain a concern. Here, we studied PM2.5 measurements made from EPA and Purple Air (PA) sensor networks in the Chicago area to understand the parameters influencing the performance characteristics of the low-cost sensor network. Using time series decomposition of PM2.5 data into short-term and baseline components using Kolmogorov–Zurbenko (KZ) filter and analysis of the extracted frequency signals, we determine that PA sensor data is more sensitive to meteorological conditions than anthropogenic activities in both short-term, and baseline components. We hypothesize that the low-cost sensor networks may have different sensitivity to aerosol from different sources and hence care must be taken in their calibrations and in their use for evaluating the impact of air quality mitigation policies.

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Section: Spectral Analysis: Methodsmentioning

confidence: 99%

Understanding the source components captured by the Purple Air Network

Kumar

Senarathna

Gurajala

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…If these abnormal data are not processed, it is likely to lead to large deviation in the prediction results. Consequently, the early data processing and analysis are essential [31]. Because there are too much data missing in stations 06, 13, 19, 20, 33, and 35, we choose to eliminate the data of these stations.…”

Section: Data Source and Analysismentioning

confidence: 99%

A Novel Hybrid Model Based on Spatiotemporal Correlation for Air Quality Prediction

Dun

Yang

Lei

2022

Mobile Information Systems

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With the rapid development of urbanization, environmental pollution has drawn worldwide attention. Accurate air quality prediction is very significant for alleviating severe pollution conditions and human healthy life. A novel hybrid model that combines a spatiotemporal correlation analysis method and an effectively simple network is proposed to forecast the concentrations of air pollutants. Firstly, a new calculation method considering distance and PM 2.5 concentrations among stations is proposed to select stations strongly correlated with the target station on the basis of grey relation analysis. Secondly, a fully convolutional network is proposed to extract spatiotemporal features efficiently based on a temporal convolutional network. In addition, meteorological factors and other pollutants are selected as auxiliary factors to further improve the prediction accuracy. To verify the validity of the proposed model, the air quality and meteorological data collected from 40 monitoring stations in Fushun, which is an important industrial city in China, are applied for air quality prediction. The performance of models is evaluated by a series of metrics. The RMSE, MAE, and R 2 values of the proposed model are 12.505, 8.214, and 0.884 for forecasting the next hour PM 2.5 concentration. Compared with other conventional models and hybrid models, the proposed model verified that it outperforms the other models with air quality prediction.

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“…Therefore, we decided to eliminate these records since the year 2021 records are added to the dataset to compensate for the shortage. Comparing methods for filling missing values and studying their impact on the forecasting results, as done in [57], [58], would be an opportunity for future work.…”

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confidence: 99%

SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

Alkhayat¹,

Hasan²,

Mehmood³

2022

Preprint

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The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

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Imputation methods for addressing missing data in short-term monitoring of air pollutants

Cited by 3 publications

References 0 publications

Understanding the source components captured by the Purple Air Network

Understanding the source components captured by the Purple Air Network

A Novel Hybrid Model Based on Spatiotemporal Correlation for Air Quality Prediction

SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

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