Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations

Fritsch, Markus; Behm, Svenia

doi:10.1016/j.atmosenv.2021.118337

Cited by 6 publications

(5 citation statements)

References 47 publications

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“…Germany was separated into 1 × 1 km grid cells and one LUR model was estimated based on monitoring sites reflecting background concentrations for NO 2 and O 3 . Overall, the models for NO 2 exhibited similar properties to the ones reported in Fritsch and Behm ( 2021a ) and highlighted agglomeration and infrastructure effects—though concentrations were generally lower. In total, air pollutant concentrations were higher for NO 2 in more densely populated areas, while the opposite was the case for O 3 .…”

Section: Methodssupporting

confidence: 73%

“…We used the land use regression (LUR) models of Fritsch and Behm ( 2021a ) to obtain estimated mean annual pollutant concentrations of NO 2 and O 3 (µg/m 3 ) for the locations where the plant material was sampled. The models are based on additive regression smoothers of spatial and structural explanatory variables and reflect the intra-urban variability (Jerrett et al 2005 ) of background concentrations for the year 2019 ( B. pendula ) and 2020 ( P. lanceolata , D. glomerata ) at the different locations.…”

Section: Methodsmentioning

confidence: 99%

“…Increases in temperature have been observed in the context of climate change, e.g., the global surface temperature increased by 1.1°C (2011–2020 compared to 1850–1900) (IPCC 2022 ). Anthropogenic activity and related changes in the surface of the earth contribute to changes in climatic conditions and are key drivers of air pollutant concentrations (Fritsch and Behm 2021a ; Kovács and Haidu 2022 ). Therefore, urban areas can be regarded as “harbingers” of climate change (Ziska et al 2003 ) and urban–rural gradients are useful for studying ecological changes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How does pollen production of allergenic species differ between urban and rural environments?

Jetschni,

Fritsch,

Jochner-Oette

2023

Int J Biometeorol

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Pollen production is one plant characteristic that is considered to be altered by changes in environmental conditions. In this study, we investigated pollen production of the three anemophilous species Betula pendula, Plantago lanceolata, and Dactylis glomerata along an urbanization gradient in Ingolstadt, Germany. We compared pollen production with the potential influencing factors urbanization, air temperature, and the air pollutants nitrogen dioxide (NO2) and ozone (O3). While we measured air temperature in the field, we computed concentration levels of NO2 and O3 from a land use regression model. The results showed that average pollen production (in million pollen grains) was 1.2 ± 1.0 per catkin of Betula pendula, 5.0 ± 2.4 per inflorescence of Plantago lanceolata, and 0.7 ± 0.5 per spikelet of Dactylis glomerata. Pollen production was higher in rural compared to urban locations on average for B. pendula (+ 73%) and P. lanceolata (+ 31%), while the opposite was the case for D. glomerata (− 14%). We found that there was substantial heterogeneity across the three species with respect to the association of pollen production and environmental influences. Pollen production decreased for all species with increasing temperature and urbanization, while for increasing pollutant concentrations, decreases were observed for B. pendula, P. lanceolata, and increases for D. glomerata. Additionally, pollen production was found to be highly variable across species and within species—even at small spatial distances. Experiments should be conducted to further explore plant responses to altering environmental conditions.

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Section: Methodssupporting

confidence: 73%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How does pollen production of allergenic species differ between urban and rural environments?

Jetschni,

Fritsch,

Jochner-Oette

2023

Int J Biometeorol

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“…Finally, an LUR model was integrated with machine learning algorithms to improve the prediction accuracy, where the linear relationships between air pollutants and explanatory variables are replaced by nonlinear relationships explored by machine learning [15,16]. For instance, non-parametric LUR models were developed with the support of a random forest model and a generalized additive model for predicting spatial distributions of ambient total particulate concentrations [42], and additive regression smoother-based LUR models were developed for investigating agglomeration and infrastructure effects on air pollutants [43]. Previous studies also have demonstrated that the accuracy of LUR and improved models-based spatial predictions, such as the prediction of particulate matter, are much higher than kriging-based models, especially for cases with relatively low numbers of observations [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Song

2022

Remote Sensing

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Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.

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“…The LUR model forecasts air pollutant concentrations in areas where monitoring stations are unavailable. These models use multiple linear regression analysis to explore the relationship between observed air pollutants and spatial factors such as traffic conditions, population, local pollution sources, land use, land cover, elevation, and distance of monitors to the sea [15][16][17][18]. Such models were developed for many cities.…”

Section: Introductionmentioning

confidence: 99%

Influence of Land Use and Meteorological Factors on PM2.5 and PM10 Concentrations in Bangkok, Thailand

et al. 2022

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Particulate matter (PM) is regarded a major problem worldwide because of the harm it causes to human health. Concentrations of PM with particle diameter less than 2.5 µm (PM2.5) and with particle diameter less than 10 µm (PM10) are based on various emission sources as well as meteorological factors. In Bangkok, where the PM2.5 and PM10 monitoring stations are few, the ability to estimate concentrations at any location based on its environment will benefit healthcare policymakers. This research aimed to study the influence of land use, traffic load, and meteorological factors on the PM2.5 and PM10 concentrations in Bangkok using a land-use regression (LUR) approach. The backward stepwise selection method was applied to select the significant variables to be included in the resultant models. Results showed that the adjusted coefficient of determination of the PM2.5 and PM10 LUR models were 0.58 and 0.57, respectively, which are in the same range as reported in the previous studies. The meteorological variables included in both models were rainfall and air pressure; wind speed contributed to only the PM2.5 LUR model. Further, the land-use types selected in the PM2.5 LUR model were industrial and transportation areas. The PM10 LUR model included residential, commercial, industrial, and agricultural areas. Traffic load was excluded from both models. The root mean squared error obtained by 10-fold cross validation was 9.77 and 16.95 for the PM2.5 and PM10 LUR models, respectively.

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Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations

Cited by 6 publications

References 47 publications

How does pollen production of allergenic species differ between urban and rural environments?

How does pollen production of allergenic species differ between urban and rural environments?

Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia

Influence of Land Use and Meteorological Factors on PM2.5 and PM10 Concentrations in Bangkok, Thailand

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