Western European Land Use Regression Incorporating Satellite- and Ground-Based Measurements of NO<sub>2</sub> and PM<sub>10</sub>

Vienneau, Danielle; Hoogh, Kees de; Bechle, Matthew J.; Beelen, Rob; Donkelaar, Aaron van; Martin, Randall V.; Millet, Dylan B.; Hoek, Gerard; Marshall, Julian

doi:10.1021/es403089q

Cited by 174 publications

(155 citation statements)

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“…In addition, the SPER is low when estimating PM 2.5 in Beijing and results in some negative concentration estimations, which is not consistent with the fact that the minimum PM 2.5 concentration should be zero [41]. In some recent studies, attempts were made to improve the accuracy of PM 2.5 estimates by using the following two approaches: (1) adding more predictor variables, e.g., on-road mobile emissions and stationary emissions data were added to LUR models in [27], satellite data were added in [33], and industry, commerce, and construction activities were added in [42]; and (2) combining LUR models with other models, e.g., a dispersion model in [36], and the Bayesian maximum entropy method in [38]. The first approach has more restrictions because the use of different regions in different countries results in different types of variables.…”

Section: Introductionmentioning

confidence: 96%

“…Thus, effective measures of preventing air pollution are indispensable to achieving sustainability. Accurate air pollution distribution is the analysis base for effective measures of preventing air the LUR model has been widely used to study the spatial distribution of environmental pollutants (such as fine particulate pollutants [24,[26][27][28][29], black carbon [30,31], NO 2 [12,[32][33][34], NO x [12], and O 3 [35]). The LUR model uses geographic information systems (i.e., spatial analysis, spatial overlay, and buffer analysis) to compute the quantitative values of several predictor variables (such as land use, road traffic, and terrain variables) for specific buffers.…”

Section: Introductionmentioning

confidence: 99%

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Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Ji-ping

Zongyi

2016

Sustainability

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Heavy air pollution, especially fine particulate matter (PM 2.5 ), poses serious challenges to environmental sustainability in Beijing. Epidemiological studies and the identification of measures for preventing serious air pollution both require accurate PM 2.5 spatial distribution data. Land use regression (LUR) models are promising for estimating the spatial distribution of PM 2.5 at a high spatial resolution. However, typical LUR models have a limited sampling point explanation rate (SPER, i.e., the rate of the sampling points with reasonable predicted concentrations to the total number of sampling points) and accuracy. Hence, self-adaptive revised LUR models are proposed in this paper for improving the SPER and accuracy of typical LUR models. The self-adaptive revised LUR model combines a typical LUR model with self-adaptive LUR model groups. The typical LUR model was used to estimate the PM 2.5 concentrations, and the self-adaptive LUR model groups were constructed for all of the sampling points removed from the typical LUR model because they were beyond the prediction data range, which was from 60% of the minimum observation to 120% of the maximum observation. The final results were analyzed using three methods, including an accuracy analysis, and were compared with typical LUR model results and the spatial variations in Beijing. The accuracy satisfied the demands of the analysis, and the accuracies at the different monitoring sites indicated spatial variations in the accuracy of the self-adaptive revised LUR model. The accuracy was high in the central area and low in suburban areas. The comparison analysis showed that the self-adaptive LUR model increased the SPER from 75% to 90% and increased the accuracy (based on the root-mean-square error) from 20.643 µg/m 3 to 17.443 µg/m 3 for the PM 2.5 concentrations during the winter of 2014 in Beijing. The spatial variation analysis for Beijing showed that the PM 2.5 concentrations were low in the north, especially in the northwest region, and high in the southern and central portions of Beijing. This spatial variation was consistent with the fact that the northern region is mountainous and has fewer people and less traffic, which results in lower air pollution, than in the central region, which has a high population density and heavy traffic. Moreover, the southern region is adjacent to Hebei province, which contains many polluting enterprises; thus, this area exhibits higher air pollution levels than Beijing. Therefore, the self-adaptive revised LUR model is effective and reliable.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Ji-ping

Zongyi

2016

Sustainability

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“…Similarly, CTMs are only valid if based on a comprehensive and detailed emission database. To overcome limitations of each of the models and optimally make use of the respective strengths, we propose to combine the two approaches into a hybrid model [43,44]. These hybrid models are usually based on the LUR model since LURs are by design much easier to modify.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area

et al. 2016

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Abstract:Two commonly used models to assess air pollution concentration for investigating health effects of air pollution in epidemiological studies are Land Use Regression (LUR) models and Dispersion and Chemistry Transport Models (DCTM). Both modeling approaches have been applied in the Ruhr area, Germany, a location where multiple cohort studies are being conducted. Application of these different modelling approaches leads to differences in exposure estimation and interpretation due to the specific characteristics of each model. We aimed to compare both model approaches by means of their respective aims, modeling characteristics, validation, temporal and spatial resolution, and agreement of residential exposure estimation, referring to the air pollutants PM 2.5 , PM 10 , and NO 2 . Residential exposure referred to air pollution exposure at residences of participants of the Heinz Nixdorf Recall Study, located in the Ruhr area. The point-specific ESCAPE (European Study of Cohorts on Air Pollution Effects)-LUR aims to temporally estimate stable long-term exposure to local, mostly traffic-related air pollution with respect to very small-scale spatial variations (ď100 m). In contrast, the EURAD (European Air Pollution Dispersion)-CTM aims to estimate a time-varying average air pollutant concentration in a small area (i.e., 1 km 2 ), taking into account a range of major sources, e.g., traffic, industry, meteorological conditions, and transport. Overall agreement between EURAD-CTM and ESCAPE-LUR was weak to moderate on a residential basis. Restricting EURAD-CTM to sources of local traffic only, respective agreement was good. The possibility of combining the strengths of both applications will be the next step to enhance exposure assessment.

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“…Novel remote sensing, satellite-based tools combined with local measurements and land-use information may become highly attractive to define air quality retrospectively and prospectively in space and time ( Fig. 2 and 3) 67 . The combination of the pollution space with individually collected information on time-activity patterns, using personal mobile micro technology, will further improve the assessment of exposure on various time scales and for specific sources.…”

Section: Interpretation Of Findingsmentioning

confidence: 99%

“…It is interesting to note that American Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1 to 10 km, altitude, and distance to sea. The models were improved using satellite-based data (reprinted with permission from Vienneau et al 67 .…”

Section: Interpretation Of Findingsmentioning

confidence: 99%

Asthma and Air Pollution. Where Are We?

Jacquemin¹,

Sunyer²

2015

BRN

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REVIEW ARTICLE BRN Reviews PERMANYERwww.permanyer.com www.brn.cat BRN Rev. 2015;1:78-91 ABSTRACT Asthma incidence and prevalence have increased in the last decades, suggesting a role of environmental factors including air pollution. In this manuscript, we summarize the evidence of the role of air pollution on (i) asthma exacerbations, (ii) asthma incidence in children, and (iii) asthma incidence in adults, pointing out the most important publications in each of these three areas. There is solid evidence that air pollution causes asthma exacerbations, characterized by hospital admissions, emergency department or doctor's visits, medication intake, or absenteeism. The evidence regarding the effect of long-term air pollution exposure on asthma onset is also quite robust. In children, many studies have associated air pollution or proximity to traffic with asthma incidence, and while there are some negative publications, the evidence for an association is appealing. In adults, several studies also point to a positive association between air pollution and asthma incidence. Furthermore, there is a lot of knowledge about the mechanisms of the different agents involved. Gases, that are oxidants, could be more associated with acute manifestations of the disease and particles, more related with chronic inflammation, could be more associated with incidence. Diesel particles have been associated with both exacerbations and incidence. However, research is still needed to better understand and identify the underlying mechanisms related to the specific pollutants and specific sources. More research is also needed to identify more susceptible groups such as males, non-smokers, or persons with another chronic condition such as atopy or obesity. Corresponding author: Bénédicte Jacquemin, bjacquemin@creal.cat

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Western European Land Use Regression Incorporating Satellite- and Ground-Based Measurements of NO₂ and PM₁₀

Cited by 174 publications

References 43 publications

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area

Asthma and Air Pollution. Where Are We?

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Western European Land Use Regression Incorporating Satellite- and Ground-Based Measurements of NO2 and PM10

Cited by 174 publications

References 43 publications

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China

Comparison of Land-Use Regression Modeling with Dispersion and Chemistry Transport Modeling to Assign Air Pollution Concentrations within the Ruhr Area

Asthma and Air Pollution. Where Are We?

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Western European Land Use Regression Incorporating Satellite- and Ground-Based Measurements of NO₂ and PM₁₀