The Context-Dependent Effect of Urban Form on Air Pollution: A Panel Data Analysis

Feili Wei,Ze Liang,Fuyue Sun,Yueyao Wang,Jiao Huang,Hong Jiang,Shuangcheng Li

doi:10.3390/rs12111793

Abstract

There have been debates and a lack of understanding about the complex effects of urban-scale urban form on air pollution. Based on the remotely sensed data of 150 cities in the Beijing-Tianjin-Hebei agglomeration in China from 2000 to 2015, we studied the effects of urban form on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations from multiple perspectives. The panel models show that the elastic coefficients of aggregation index and fractal dimension are the highest among all factors for the whole region. Population density, aggregation index, and fractal dimension have stronger influences on air pollution in small cities, while area size demonstrates the opposite effect. Population density has a stronger impact on medium/high-elevation cities, while night light intensity (NLI), fractal dimension, and area size show the opposite effect. Low road network density can enlarge the influence magnitude of NLI and population density. The results of the linear regression model with multiplicative interactions provide evidence of interactions between population density and NLI or aggregation index. The slope of the line that captures the relationship between NLI on PM2.5 is positive at low levels of population density, flat at medium levels of population density, and negative at high levels of population density. The study results also show that when increasing the population density, the air pollution in a city with low economic and low morphological aggregation degrees will be impacted more greatly.

Highlights

In recent years, in addition to tracing emissions from pollution sources and encouraging changes in energy structure, the linkage between urban form and air quality has become a hot topic in the context of global urbanization and urban air pollution, which are some of the largest environmental health threats [1,2]
We modeled the relationships between APIs and urban form indicators (UFIs): ln(APIit) = μ + β ln(UFIit) + Ui + εit where APIit is the air pollution indicator of the tth year at the ith urban unit; μ is a scalar coefficient; β is a vector of the parameters; Ui denotes the individual effect of the ith urban unit, capturing the idiosyncratic characteristics of each urban unit; εit denotes the random error of the tth year at the ith urban unit; and UFIit is a vector of the urban form factors
The corresponding formula of this model is as follows: ln(APIit) = μ + β1 ln(UFIit) + β2 ln(Dit) ln(Xit) + Ui + εit where APIit is the API of the tth year at the ith urban unit; μ is a scalar coefficient; β is a vector of the parameters; Ui denotes the individual effect of the ith urban unit, capturing the idiosyncratic characteristics of each urban unit; εit denotes the random error of the tth year at the ith urban unit; and UFIit is a vector of the urban form factors

Summary

Introduction

In addition to tracing emissions from pollution sources and encouraging changes in energy structure, the linkage between urban form and air quality has become a hot topic in the context of global urbanization and urban air pollution, which are some of the largest environmental health threats [1,2]. A study of 45 metropolitan areas in the United States showed that the increase in urban density can reduce NO2 emissions by controlling the factors of population size and temperature [6]. Another study of 111 cities in the United States showed that higher population density (POPDEN) led to higher FINE particulate matter (PM2.5) concentration and air quality index (AQI) values per capita [9]. A study of 83 cities around the world showed that the decrease in NO2 caused by a 4% increase in urban continuity can offset the negative impact of a 10% increase in population size [10,11]. More inconsistencies can be found in studies around the world [7]

Methods

Results

Discussion

Conclusion