Evidence map›Paper›PMID 33411307›Full record

ArticleEnvironmental science and pollution research international2021

A new buffer selection strategy for land use regression model of PM

Zeyu Liu, Qingyu Guan, Jinkuo Lin, Liqin Yang, Haiping Luo, Ning Wang

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Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Zeyu LiuGansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
Qingyu GuanGansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China. guanqy@lzu.edu.cn.
Jinkuo LinGansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
Liqin YangGansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
Haiping LuoGansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.
Ning WangGansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China.

Funding

National Natural Science Foundation of China 41671188
6 · The paper itself

Abstract

In order to calculate the spatial distribution of high-resolution air-pollutant levels, the land use regression (LUR) model can be an effective method due to the comprehensive consideration of various factors. Traditional LUR models mostly use predefined buffers, which have the disadvantage of not matching high-resolution data well. In order to get a better-fitting model, a few researches have proposed new buffer selection methods. To solve this problem, we propose a new optimal buffer selection method based on the dichotomy to improve the correlation between predicted variables and pollutant concentration. For some socioeconomic data with high spatial resolution that cannot be obtained, for example, building data is used instead of population density data. Compared with the model with the predefined buffers, the model with our buffer selection strategy explained additional 5% variability in measured concentrations, in terms of the R

Indexed as

Air PollutantsAir PollutionChinaEnvironmental MonitoringParticulate MatterAir PollutantsParticulate MatterBuffer selection strategyFine particulate matterLand useLand use regressionPoint of interestRoad network

Identifiers

PMID33411307

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.