Characterizing the spatiotemporal variability of PM2.5 in Cusco, Peru using kriging with external drift

Pearce, John L.; Rathbun, Stephen L.; Aguilar-Villalobos, Manuel; Naeher, Luke P.

doi:10.1016/j.atmosenv.2008.10.060

Cited by 50 publications

(43 citation statements)

References 21 publications

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“…Taking account of these validation results, regression kriging outperformed the linear regression methods, which is consistent with the reported results (Beelen et al, 2009;Pearce et al, 2009;Mercer et al, 2011) as described earlier.…”

Section: Prediction Performancesupporting

confidence: 80%

“…This procedure has been applied in many previous studies for model validations (e.g., Pearce, et al, 2009;De Smit et al, 2010). In this method, one measurement point is removed and then the concentration at that point is predicted by using the rest of the points.…”

Section: Validationmentioning

confidence: 99%

“…There are other terms such as universal kriging and kriging with external drift (KED), but these methods including regression kriging are basically the same technique (Hengl et al, 2003). Some studies compared regression kriging with the regression method and reported that the prediction performance of regression kriging was better (Beelen et al, 2009;Pearce et al, 2009;Mercer et al, 2011). This method has been successfully applied at the EU-wide scale at 1 × 1 km resolution for the prediction of annual means of air pollutant concentrations (Beelen et al, 2009), which would be computationally demanding for chemical transportation models.…”

Section: Introductionmentioning

confidence: 99%

“…A few studies have applied regression kriging for the prediction of the spatial distribution of air pollutants (e.g., Beelen et al, 2009;Pearce et al, 2009). Regression kriging is a geostatistical approach -ordinary kriging with the residuals of regression analysis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application of Regression Kriging to Air Pollutant Concentrations in Japan with High Spatial Resolution

Araki

Yamamoto

Kondo

2015

Aerosol Air Qual. Res.

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The application of regression kriging to air pollutants in Japan was examined for the purpose of providing a practical method to obtain a spatial distribution with sufficient accuracy and a high spatial resolution of 1 × 1 km. We used regulatory air monitoring data from the years 2009 and 2010. Predictor variables at 1 × 1 km resolution were prepared from various datasets to perform regression kriging. The prediction performance was assessed by indicators, including root mean squared error (RMSE) and R 2 , calculated from the leave-one-out cross validation results, and was compared to the results obtained from a linear regression method, often referred to as land use regression (LUR). Regression kriging wellexplained the spatial variability of NO 2 , with R 2 values of 0.77 and 0.78. Ozone (O 3 ) was moderately explained, with R 2 values of 0.52 and 0.66. The reason for this difference in performance between NO 2 and O 3 might be the characteristics of these pollutants -primary or secondary. Regression kriging outperformed the linear regression method with regard to RMSE and R 2 . The performance of regression kriging in this work was comparable to that found in previous studies. The results indicate that regression kriging is a practical procedure that can be applied for the prediction of the spatial distribution of air pollutants in Japan, with sufficient accuracy and a high spatial resolution.

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Section: Prediction Performancesupporting

confidence: 80%

Section: Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Regression Kriging to Air Pollutant Concentrations in Japan with High Spatial Resolution

Araki

Yamamoto

Kondo

2015

Aerosol Air Qual. Res.

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“…Although in the 1990s there were some studies in the literature on travel demand forecasting with applications of estimation techniques related to Kriging (Ickstadt, K. et al, 1998), its application is still recent and has not yet been fairly explored in the line of research of Transportation Planning (Yoon et al, 2014;Chen et al, 2015). However, it is possible to find diverse studies in "Transportation Engineering" field (Ciuffo et al, 2011;Mazzella et al, 2011;Zhang and Wang, 2013) and also Emission of vehicular gases (Pearcea et al, 2009;Kassteele and Velders, 2006;Kassteele and Stein, 2006). Different levels of data aggregation, known in the field of spatial analysis as the change of support, influence the results of geostatistical analysis.…”

Section: Introduction and Brief Literature Reviewmentioning

confidence: 99%

Proposal of a Geostatistical Procedure for Transportation Planning Field

2017

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Abstract:The main objective of this study is to estimate variables related to transportation planning, in particular transit trip production, by proposing a geostatistical procedure. The procedure combines the semivariogram deconvolution and Kriging with External Drift (KED). The method consists of initially assuming a disaggregated systematic sample from aggregate data. Subsequently, KED was applied to estimate the primary variable, considering the population as a secondary input. This research assesses two types of information related to the city of Salvador (Bahia, Brazil): an origin-destination dataset based on a home-interview survey carried out in 1995 and the 2010 census data. Besides standing out for the application of Geostatistics in the field of transportation planning, this paper introduces the concepts of semivariogram deconvolution applied to aggregated travel data. Thus far these aspects have not been explored in the research area. In this way, this paper mainly presents three contributions: 1) estimating urban travel data in unsampled spatial locations; 2) obtaining the values of the variable of interest deriving out of other variables; and 3) introducing a simple semivariogram deconvolution procedure, considering that disaggregated data are not available to maintain the confidentiality of individual data.

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Estimating vehicle miles traveled (VMT) in urban areas using regression kriging

Kim

Park

Heo

et al. 2016

J of Advced Transportation

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The recent increase in demand for performance-driven and outcome-based transportation planning makes accurate and reliable performance measures essential. Vehicle miles traveled (VMT), the total miles traveled by all vehicles on roadways, has been utilized widely as a proxy for traffic impact assessment, vehicle emissions, gasoline consumption, and crashes. Accordingly, a number of studies estimate VMT using diverse data sources. This study estimates VMT in the urban area of Bucheon, South Korea, by predicting the annual average daily traffic for unmeasured locations using spatial interpolation techniques (i.e., regression kriging and linear regression). The predictive performance of this method is compared with that of the existing Highway Performance Monitoring System (HPMS) method. The results show that regression kriging could provide more accurate VMT estimates than the HPMS method and linear regression, especially with a small sample size. additional control variables. Pathomsiri et al. [16] develop an econometric model for estimating VMT in multivehicle households and apply it to the 2001 National Household Transportation Survey. Erlbaum [17] applied the fuel-sales-based method to estimate VMT. White [18] and Greene [19] used odometer reading data to estimate annual VMT by driver. However, the previously mentioned methods, based on household activity surveys, fuel sales, and odometer reading data, are too resource-intensive and costly to perform regularly. Also, these methods reflect personal travel and do not reflect the total number of vehicles on the road.Traffic-count-based VMT estimation methods are currently the most commonly performed and preferred method because they are based on actual data for vehicle movement [20]. The predictive accuracy of traffic-count-based methods depends on the quality and coverage of traffic count data, given that the length of all road network sections is known. Thus, it can be said that if traffic counts were available for all roads in a network, the VMT estimate would be the most accurate measure of vehicle movement. Traffic counts are, however, only available at segments of road networks where there is a count station because of the relatively high cost of the stations. For urban areas, traffic count data are collected less frequently than in other areas because of the complexity of measurement [21]. For this reason, the Highway Performance Monitoring System (HPMS) method of VMT estimation, a representative traffic-count-based method performed in the USA, simply extrapolates the VMT of a sample section into other sections, so long as the other sections are in the same strata of traffic volume group and road functional system [20]. Despite the advantages of being a relatively simple and quick procedure, the HPMS method has been criticized by its accuracy and sample size demand needed to achieve the required precision level. Moreover, stratification of sampling by traffic volume group requires knowledge of the traffic count information on all roads [14]. A number of rec...

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Characterizing the spatiotemporal variability of PM2.5 in Cusco, Peru using kriging with external drift

Cited by 50 publications

References 21 publications

Application of Regression Kriging to Air Pollutant Concentrations in Japan with High Spatial Resolution

Application of Regression Kriging to Air Pollutant Concentrations in Japan with High Spatial Resolution

Proposal of a Geostatistical Procedure for Transportation Planning Field

Estimating vehicle miles traveled (VMT) in urban areas using regression kriging

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