A spatial land use clustering framework for investigating the role of land use in mediating the effect of meteorology on urban air quality

Montazeri, Amir; Lilienthal, Achim J.; Albertson, J. D.

doi:10.1016/j.aeaoa.2021.100126

Cited by 2 publications

(2 citation statements)

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“…First of all, the current study reflects the association between tree diversity, urban form, and NO 2 concentration, but the causality could be further analyzed through Rubin or Pearl's structural casual models with modulators, such as traffic intensity and collection periods. Therefore, future studies could classify the mobile data by the TAZ (Traffic Analysis Zone) or the measurement time (e.g., weekdays and weekends or daynight division) (Montazeri et al, 2021). Secondly, the upper bounds of buffer distances obtained from lacunarity analysis could be further validated by analyzing the influential range of emission sources through deterministic models, such as Gaussian plume dispersion (Gibson et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Improving NO2 prediction by integrating tree diversity, urban form, and scale sensitivity through mobile monitoring

et al. 2023

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Section: Discussionmentioning

confidence: 99%

Improving NO2 prediction by integrating tree diversity, urban form, and scale sensitivity through mobile monitoring

et al. 2023

View full text Add to dashboard Cite

“…Therefore, future studies could classify the mobile data by the TAZ (Tra c Analysis Zone) or the measurement time (e.g., weekdays and weekends or day-night division) (Montazeri et al, 2021).…”

Section: Declarations Acknowledgmentmentioning

confidence: 99%

Incorporating tree diversity for a better understanding of urban form-air quality relationships through mobile monitoring

Tian

Wang

Mora

et al. 2022

Preprint

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Context: Air pollution is a major threat to landscape ecology and public health. The conventional LUR (Land Use Regression) method has been developed mostly based on 2-D urban form and emission source information. Besides, the effects of tree diversity on air quality have not been adequately addressed. Objectives: This study explores the integration of 2-D and 3-D urban form metrics and examines the impact of tree diversity in urban form-air quality relationships. Methods: We introduce these predictors into the LUR tools using unique NO2 datasets collected through opportunistic mobile monitoring in the Bronx, New York, and Oakland, California, and further apply lacunarity to investigate the spatial scale sensitivity for regression predictors. Results: The lacunarity-optimized model helps to reduce the computation burden by finding the upper limit of the spatial heterogeneity of predictors while keeping the model accuracy in both Bronx (R2≈0.50) and Oakland (R2≈0.79). Furthermore, although deciduous trees are surrounded by the highest NO2 concentrations (9.73 ppb and 3.61 ppb in the Bronx and Oakland, respectively), the increase in tree diversity could facilitate the reduction of NO2 concentration. However, according to the non-monotonic and marginal effects of tree diversity on NO2 concentration, a higher level of tree diversity is not always better. Conclusions: It is reasonable to seek a balance between the diversity and dominance of tree species to effectively improve air quality on the city scale. The findings are useful for both environmental scientists striving for better air quality and urban planners that care for the well-being of cities.

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A spatial land use clustering framework for investigating the role of land use in mediating the effect of meteorology on urban air quality

Cited by 2 publications

References 37 publications

Improving NO2 prediction by integrating tree diversity, urban form, and scale sensitivity through mobile monitoring

Improving NO2 prediction by integrating tree diversity, urban form, and scale sensitivity through mobile monitoring

Incorporating tree diversity for a better understanding of urban form-air quality relationships through mobile monitoring

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