Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics

Idir, Yacine Mohamed; Orfila, Olivier; Judalet, Vincent; Sagot, Benoît; Chatellier, Patrice

doi:10.3390/s21144717

Cited by 10 publications

(8 citation statements)

References 51 publications

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“…This approach is based on the ST-KED estimator and the form of the target variable Z, as represented in Equation ( 1). The ST-KED system is written as [15]:…”

Section: Spatio-temporal Kriging With External Driftmentioning

confidence: 99%

“…Similar to the ST-RK method, the target variable in the spatio-temporal kriging with external drift (ST-KED) process can be divided into deterministic drift and stochastic residual terms. However, unlike the ST-RK method, ST-KED weights are directly derived from the kriging equations, which are formulated under the unbiasedness and minimum error variance conditions [15].…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Dual Kriging with Adaptive Coefficient Drift Function

Kongsanun,

Chutsagulprom,

Moonchai

2024

Mathematics

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Research on spatio-temporal geostatistical modeling remains a critical challenge in numerous scientific and engineering disciplines. This paper introduces a novel extension of dual kriging, called spatio-temporal dual kriging (ST-DK), in which drift functions with fixed and adaptive coefficients are established. The approach appears to be effective in modeling complex spatio-temporal dynamics, particularly when relevant auxiliary variables exert substantial influence on the target variable. To illustrate its performance, we compare the ST-DK model with the classical spatio-temporal regression kriging (ST-RK) and geographically and temporally weighted regression (GTWR) models for estimating temperature and air pressure data from Thailand in 2018. Our findings demonstrate that both the ST-DK and ST-RK models when utilizing adaptive coefficients outperform their fixed coefficient counterparts. Furthermore, the ST-DK method consistently exhibits superior performance compared to the ST-RK and GTWR methods.

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“…This approach is based on the ST-KED estimator and the form of the target variable Z, as represented in Equation ( 1). The ST-KED system is written as [15]:…”

Section: Spatio-temporal Kriging With External Driftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Dual Kriging with Adaptive Coefficient Drift Function

Kongsanun,

Chutsagulprom,

Moonchai

2024

Mathematics

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“…However, the one‐dimensional interpolation has been found to be susceptible to generating inaccurate outcomes as it neglects the inclusion of important information in the other dimension (Cheng et al., 2018; Deng et al., 2018). Thus, several ST ensemble methods have been developed, such as ST‐IDW (Reynolds & Madden, 1988), ST‐kriging (Bae et al., 2018; Idir et al., 2021), ST‐PBSHADE (Xu et al., 2022), geographical and temporal weighted regression (Fotheringham et al., 2015), and IDW‐SES (Cheng & Lu, 2017). These methods can be considered as an expansion of the traditional spatial interpolation method into the temporal dimension.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal stacking method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records

Chen,

2024

Transactions in GIS

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The reliability of hourly PM2.5 data obtained from air quality monitoring stations is compromised as a result of the missing values, thereby impeding the thorough examination of crucial information. In this paper, we present a spatiotemporal (ST) stacking machine learning (ML) method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records. First, the ST neighbors for the target station with missing values are selected at a daily scale. Subsequently, the non‐null data within the ST neighbors undergo an iterative P‐BSHADE interpolation process for re‐interpolation. Next, a stacking ML model is constructed using the re‐interpolation values and several environmental factors associated with PM2.5 as the predictors, while the observed PM2.5 is taken as the independent variable. Finally, the missing values are reconstructed by inputting the predictors into the trained stacking model. The study utilized hourly PM2.5 data in the Beijing‐Tianjin‐Hebei region as a case study to assess the effectiveness of the proposed method, using daily missing ratios of 10%, 30%, and 50%, respectively. The accuracy of the proposed method was then compared to four contemporary ST interpolation methods. The results indicate that the proposed method exhibits superior performance compared to the classical methods. Specifically, it achieves a reduction in the average root mean square error and mean absolute error by at least 40.6% and 40.1%, respectively. Additionally, the proposed method demonstrates the successful recovery of extreme values in the hourly PM2.5 records, in contrast to the classical methods which often exhibit a tendency to overestimate low values and underestimate high values. Overall, the proposed method presents a viable and efficient approach to recover missing values in the hourly PM2.5 records that demonstrate evident daily periodic patterns.

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“…At the same time, drive-by sensing has introduced a range of novel research challenges in terms of sensor deployment [11] [12], spatiotemporal coverage [13], data collection strategies [14][15] [16], calibration models [10] [17], and data analysis [18] [19]. For example, the predictable nature of bus routes and schedules presents new opportunities that could be exploited for optimizing spatial coverage.…”

Section: Introductionmentioning

confidence: 99%