Spectral methods for imputation of missing air quality data

Moshenberg, Shai; Lerner, Uri; Fishbain, Barak

doi:10.1186/s40068-015-0052-z

Cited by 19 publications

(6 citation statements)

References 31 publications

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“…One of the limitations of this study was missing air pollutant data. Missing data is a frequent problem in many scientific fields, especially in studies about the effects of ambient air pollutants [ 34 , 58 ]. Missing data is common in air quality monitoring stations due to unpredicted technical malfunctions or faulty equipment, that effect data storage [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Associations of short-term exposure to air pollution with respiratory hospital admissions in Arak, Iran

Vahedian

Khanjani

Mirzaee

et al. 2017

J Environ Health Sci Engineer

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BackgroundAmbient air pollution, is one of the most frequently stated environmental problems. Many epidemiological studies have documented adverse health effects for ambient air pollution. This study aimed to investigate the association between ambient air pollution and respiratory hospital admissions.MethodsIn this ecological time series study data about air pollutant concentrations including CO, NO2, O3, PM2.5, PM10 and SO2 and, respiratory hospital admissions in the urban population of Arak, from January 1st 2010 to December 31st 2015; were inquired, from the Arak Department of Environment, and two major hospitals, respectively. Meteorological data were inquired for the same period as well. Time-series regression analysis with a distributed lag model, controlled for seasonality long-time trends, weather and day of the week, was used for data analysis.ResultsEvery 10 μg/m3 increase in NO2, and PM10 and every 1 mg/m3 increase in CO at lag 0 corresponded to a RR = 1.032 (95%CI, 1.003–1.06), RR = 1.01 (95%CI, 1.004–1.017) and RR = 1.09 (95%CI, 1.04–1.14), increase in respiratory disease hospitalizations, respectively. Males and the elderly were found to be more susceptible than females and other age groups to air pollutants in regard to respiratory disease admissions.ConclusionsThe results of this study showed that outdoor air pollutants significantly increase respiratory hospital admissions; especially among the men and elders in Arak.

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Section: Discussionmentioning

confidence: 99%

Associations of short-term exposure to air pollution with respiratory hospital admissions in Arak, Iran

Vahedian

Khanjani

Mirzaee

et al. 2017

J Environ Health Sci Engineer

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“…We are aware of that being a complicated task and we stress the fact that this was essential for testing the sole feasibility of the idea. Now, with this concept proven, we aim at lowering the number of angles needed for the reconstruction as was shown in [35] and [36]. A good challenge will also be testing what will be the turning point in terms of accuracy where lowering the number of cameras downgrades the method greatly.…”

Section: Discussionmentioning

confidence: 99%

Mathematical Estimation of Particulate Air Pollution Levels by Aerosols Tomography

2022

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Air pollution control and mitigation are important factors in wellbeing and sustainability. To this end, air pollution monitoring has a significant role. Today, air pollution monitoring is mainly done by standardized stations. The spread of those stations is sparse and their cost hinders the option of adding more. Thus, arises the need for cheaper and available means to assess air pollution. In this article, a mathematical method to solve the inverse problem of aerosols tomography is proposed. The suggested method applies filtered back-projection method on a pixel-wise blur estimation. Using the method, particles' concentrations in a 3D space is reconstructed from photos taken from different angles. The proposed method is shown to be very effective for assessing air pollution levels by means of multi angle imaging. Specifically, estimating images' blur as an indication for Particulate Matter (PM) ambient levels. The results of the research point towards strong correlation between image

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“…Imputation methods will be compared, and significance between them assessed, using the metric of MAD between the imputed and observed values. This scalar measure allows for the imputation methods to be ranked in a manner similar to root mean square error or average error. Comparisons will be made at the feature level because features vary according to multiple characteristics, and some imputation methods may perform better for different features.…”

Section: Comparison and Assessmentmentioning

confidence: 99%

“…Multiple imputation is a hot deck approach where multiple imputations are multiple draws from an estimated distribution. This approach is not an effective method for time series data where the value at a given time is dependent upon its location in the time series, and the methods do not inherently take advantage of the dependencies within time series data. In addition, the EM algorithm assumes that the missing data are linearly related to the observed data, which we do not expect in our example.…”

Section: Introductionmentioning

confidence: 99%

Imputation for multisource data with comparison and assessment techniques

Casleton

Osthus

Buren

2017

Appl Stoch Models Bus & Ind

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Missing data are prevalent issue in analyses involving data collection. The problem of missing data is exacerbated for multisource analysis, where data from multiple sensors are combined to arrive at a single conclusion. In this scenario, it is more likely to occur and can lead to discarding a large amount of data collected; however, the information from observed sensors can be leveraged to estimate those values not observed. We propose two methods for imputation of multisource data, both of which take advantage of potential correlation between data from different sensors, through ridge regression and a state-space model. These methods, as well as the common median imputation, are applied to data collected from a variety of sensors monitoring an experimental facility. Performance of imputation methods is compared with the mean absolute deviation; however, rather than using this metric to solely rank the methods, we also propose an approach to identify significant differences. Imputation techniques will also be assessed by their ability to produce appropriate confidence intervals, through coverage and length, around the imputed values. Finally, performance of imputed datasets is compared with a marginalized dataset through a weighted k-means clustering. In general, we found that imputation through a dynamic linear model tended to be the most accurate and to produce the most precise confidence intervals, and that imputing the missing values and down weighting them with respect to observed values in the analysis led to the most accurate performance. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. Appl. Stochastic Models Bus. Ind. 2018, 34 44-60 E. CASLETON ET AL.improved estimation and accuracy and more precise inferences [5], assuming the data has been optimally combined [6]. Multisource data analysis is similar or analogous to techniques such as multimodality [7], multi-intelligence, data fusion [5], and sensor fusion [8]. These forms of analysis are common in diverse research areas, such as problems of interest to the Department of Defense [5], robotics [9], and biomedical research [7].For two main reasons, the problem of missing data is exacerbated in the multisource regime. First, because the analysis will include data from multiple sensors, there is a higher probability that at least one will fail at some point during the study, leading to missing data. Secondly, if data are missing from a single sensor at, say, time t, and the default method of marginalization is implemented, observed data from all other sensors at time t will not contribute to an analysis. Further, if data are missing from different sensors at different times, the total analyzed sample size could be drastically reduced from that collected. Often, the types of data explored in multisource analysis are expensive to collect and store, thus, marginalization is not a good option for missing multisource data in terms of resource utilization.Alternatively, the multisource setting provides an opportunit...

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Spectral methods for imputation of missing air quality data

Cited by 19 publications

References 31 publications

Associations of short-term exposure to air pollution with respiratory hospital admissions in Arak, Iran

Associations of short-term exposure to air pollution with respiratory hospital admissions in Arak, Iran

Mathematical Estimation of Particulate Air Pollution Levels by Aerosols Tomography

Imputation for multisource data with comparison and assessment techniques

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