Land Use Regression Models for Particle Number Concentration and Black Carbon in Lanzhou, Northwest of China

Shuya Fang, Tian Zhou (), Limei Jin, Xiaowen Zhou, Xingran Li, Xiaokai Song and Yufei Wang
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Shuya Fang: College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Tian Zhou: College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Limei Jin: School of Public Health, Gansu University of Chinese Medicine, Lanzhou 730000, China
Xiaowen Zhou: College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Xingran Li: College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Xiaokai Song: College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Yufei Wang: College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China

Sustainability, 2023, vol. 15, issue 17, 1-17

Abstract: It is necessary to predict the spatial variation in particle number concentration (PNC) and black carbon (BC) because they are considered air pollutants associated with traffic and many diseases. In this study, land use regression (LUR) models for PNC and BC were developed based on a mobile monitoring campaign in January 2020 in Lanzhou, and the performance of models was evaluated with hold-out validation (HV) and leave-one-out cross-validation (LOOCV). The results show that the adjusted R 2 of the LUR models for PNC and BC are 0.51 and 0.53, respectively. The R 2 of HV and LOOCV are 0.43 and 0.44, respectively, for the PNC model and 0.42 and 0.50, respectively, for the BC model. The performances of the LUR models are of a moderate level. The spatial distribution of the predicted PNC is related to the distance from water bodies. The high PNC is related to industrial pollution. The BC concentration decreases from south to north. High BC concentrations are associated with freight distribution centres and coal-fired power plants. The range of PNC particle sizes in this study is larger than in most studies. As one of few studies in Lanzhou to develop LUR models of air pollutants, it is important to accurately estimate pollutant concentrations to improve air quality and provide health benefits for residents.

Keywords: land use regression; mobile monitoring; particle number concentration; black carbon (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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