Analysis of spatial and seasonal distributions of air pollutants by incorporating urban morphological characteristics
Introduction
Since the rapid urbanization and industrialization, China has experienced deteriorating air condition, especially in large cities during the past two decades. For Chinese cities, the emissions of NO2 (nitrogen dioxide) and SO2 (sulfur dioxide) in 2010 were as 2.5 times and 1.5 times the values in 1990, respectively (Mendenhall, Sincich, & Boudreau, 1996). Among them all, the most common air pollutant is PM2.5 in recent years and a lot of attention has been paid to it (Wang, Hu, Chen, Chen, & Xu, 2013, Yang et al., 2017, Yuan, Liu, Castro, & Pan, 2012). In Beijing, the annual average concentration of PM2.5 was around 70–100 μg/m3, which were two to three times higher than the level 1 Interim Target (35μg/m3) assigned by WHO (Cheng et al., 2013). Besides, these emissions have adversely impacted air quality at regional, national, and even global scales and played an essential role in global climate change (Grimm et al., 2008). In addition, air pollutants have been damaging the environment due to their chemical properties. For example, the acidification of soil, lakes, and rivers are resulted from NO, SO2, and Ammonia, which caused the loss of plant life, the habitat of animals, and the reduction of crop yield (Rao, Rajasekhar, & Rao, 2014). Moreover, annually, almost 3.7 million people die prematurely because of outdoor air pollution around the world (WHO, 2014). In China, air pollution has become the fourth greatest risk factor in all deaths (Fang, Liu, Li, Sun, & Miao, 2015). Many epidemiological researchers have demonstrated that air pollution leads to a variety of health problems by long-term exposure to air pollutants (Brunekreef & Holgate, 2002), which produces the considerable medical cost for individuals and economic loss for the government by the reduction of productivity.
Air pollutants are released from a variety of sources, such as industry and transportation, and are driven by various social economic factors. Many researchers believe air pollution is closely related to the urban land use patterns and optimization of spatial planning could improve the air quality in the long run. Thus, the relationship between the spatial land use and land cover and the distribution of air pollution attracts our attention. The conception of LUR (Land Use Regression) was first introduced and termed regression mapping (Briggs et al., 1997) and was applied in the SAVIAH (Small Area Variation in Air Quality and Health) project. The development and accessibility of GIS data and techniques contributed a lot to the popularity of LUR models. Ross et al. (2007) predicted PM2.5 in New York City and surrounding counties by LUR models, integrated with traffic density and census data. The model interpreted 60% or more of the variation of PM2.5 concentration with prediction errors below 10% (Ross, Jerrett, Ito, Tempalski, & Thurston, 2007). Meng et al. combined population, length of major roads, agricultural land area, and the number of the industry sites to the LUR model and explained a large part of the variability of NO2 concentration in Shanghai, which outperformed the interpolation methods of Kriging and IDW (Meng et al., 2015). Liu, Henderson, Wang, Yang, and Peng (2016) used LUR model to interpret the variances of NO2 and PM2.5 concentrations in Shanghai, and found anti-correlation with coastal regions and correlation with industry and highway intensity (Liu et al., 2016). Zheng et al. analyzed the land use patterns with the spatiotemporal distribution of multiple air pollutants (i.e. O3, SO2, NO2, and CO) to find their relationships (Zheng et al., 2017). To improve the temporal granularity of air pollution monitoring data, Anand et al. built a mixed-effects LUR method to model daily NO2 concentrations in Hong Kong from 2005 to 2015 with satellite datasets, which realized the daily mapping of ambient surface NO2 (Anand & Monks, 2017). When it comes to the global scale, Larkin et al. created the first global NO2 LUR model to find the spatial variability of NO2 concentration. The model explained 54% of the annual change of NO2 and continental R2 ranges from 0.42 to 0.67 (Larkin et al., 2017).
Some studies constructed multiple LUR models for different scenarios or purposes. For example, Wu et al. constructed eight LUR models to explain diurnal, seasonal and annual spatial changes of PM2.5 concentration in Beijing (Wu et al., 2015). Huang et al. explored the relationship of fifty-nine potential variables (e.g. land use, traffic and industry emission, and population density) with four air pollutants (i.e. PM2.5, SO2, NO2, and O3) through LUR models based on national monitoring network, and the variance of the four pollutants could be explained to a certain degree (Huang et al., 2017a). Yang et al. set four LUR models to estimate the air pollution concentration through ground-based measurements, remote sensing data, air quality model, and other spatial inputs, and found the best model to explain the NO2 and PM2.5, respectively (Yang et al., 2017). Yang et al. developed seasonal LUR models to simulate the change of PM2.5 in the urban area, and these models had a good fit and explained the variation of spatial distribution of PM2.5 concentration well (Yang et al., 2017). However, such multiple LUR models may not always show good performance. For instance, Muttoo et al. stated that seasonal models did not show clear differences for measuring NOx with similar R2 values, and this was due to the high correlation between seasonal measurements for each of the monitoring sites (Muttoo et al., 2018).
Even though the spatial land use patterns are related to the distribution of air pollutants, the accuracy of the final results depends heavily on the accuracy of classification of land use types and most of them interpreted the results based on LUR model only. Besides, the urban morphology, as the three-dimension form of a set of buildings and urban shapes (Chen, 2013), has significant influences on the concentration of air pollution (Cárdenas Rodríguez, Dupont-Courtade, & Oueslati, 2016). Bereitschaft et al. quantified urban form by preexisting sprawl indices and spatial metrics and analyzed their relationship with air pollution among 86 U.S. cities (Bereitschaft & Debbage, 2013). Yuan et al. quantified the impact of urban morphological parameters, urban permeability, and building geometries, on the dispersion of air pollution by CFD (Computational fluid dynamics) approach in the high-density urban regions (Yuan, Ng, & Norford, 2014). Rodríguez et al. proved that urban morphology produced significant influences on pollution centration and found that higher concentration of NO2 and PM10 was related to fragmented and highly constructed cities and the higher concentration of SO2 was related to densely populated cities (Cárdenas Rodríguez et al., 2016). She et al. (2017) analyzed the correlation between urban form which was described by six spatial metrics and group-based measurements of six air pollutants in the largest metropolitan zone, Yangtze River Delta. The study demonstrated that urban form did affect the urban air quality (She et al., 2017). Lu et al. developed the geographically weighted regression model to analyze the relationship between urban form and the density of NO2 and SO2 derived by satellite data, and the results showed that urban form produced significant effects on the air quality in urban areas of China (Lu & Liu, 2016).
As for street-level analysis, Maignant evaluated the dispersion of air pollution through the MISKAM (Mikroskaliges Klima und Ausbreitungsmodell) model integrating with urban morphology, buildings volume and roughness, and climatology factors in a street canyon (Maignant, 2006). Edussuriya et al. found the linkage between urban morphology and air quality, and the final results showed that six morphological variables significantly explained the variance of air pollution at the street level (Edussuriya, Chan, & Ye, 2011). Shen et al. investigated the connection of street morphology or canyons to dispersion of air pollution for six cities around the world. It was concluded that an open central street would greatly improve the air quality due to a larger vertical exchange of air flow via the street roof (Shen, Gao, Ding, & Yu, 2017). Shi et al. used vehicle-based mobile measurements and regression models to estimate the spatial distribution of PM2.5 and PM10, and found the most decisive factor of urban morphology was the frontal area index on street-level air quality in the central area of Hong Kong (Shi et al., 2016a).
However, these previous studies emphasized those building morphology parameters (e.g. sky view factor (Shi et al., 2016b, Silva & Monteiro, 2016), and building orientation (Lu & Liu, 2016)) or urban form indices (e.g. frontal area index (Ghassoun & Löwner, 2017, Lu & Liu, 2016, Shi et al., 2016a)) that are largely affected by meteorological factors such as wind direction and wind speed, or by the detailed building fabrics, which is more suitable for small research areas with clear 3D building maps and accurate group-based field measurements. For a megacity like Beijing, this process would be time-consuming and need too much human interference. This current study proposes to investigate morphological characteristics at various spatial levels based on data extracted from high-resolution (HR) images. Such urban form characteristics has not been investigated before in statistical approaches to analyzing the spatiotemporal distribution of air pollution in megacities. Another shortcoming in many of the previous LUR models is the assumption of linear relationship. Because any type of models (e.g. LUR model) may have its own limitations due to its assumption (e.g. linearity) and internal mechanism, the current study will apply multiple models to supplement each other in order to provide findings for a better understanding.
This study aims to answer three questions for the study area: (1). What is the potential utility of morphological information for estimating the spatiotemporal distribution of air pollution? (2). Are there seasonal differences in the distributions of air pollutants and if so how are these differences distributed spatially? (3). To what extent does each factor of interest affects the spatial and seasonal distributions of air pollutants? The innovation of the research lies in four aspects: i) it is the one of the first studies that incorporates urban morphological characteristic, which is not sensitive to the accuracy of land use classification and is easy for general adoption, as an essential predictor for air pollution distribution. We combine morphological, meteorological, land use and socioeconomic variables together to analyze the distribution of air pollutants' concentrations; ii) the research is designed carefully in consideration of possible sensitivity of parameter choices, thus the approach is generally applicable to study other cities; iii) parallel analyses of spatial distribution of seven air pollution indexes are conducted simultaneously for both warm and cold seasons, which helps to capture the changing dynamics of air pollutants throughout the year; iv) the use of both spatiotemporal regression and SSH models provides a more comprehensive understanding of how air pollutants concentrations are affected by different factors.
Section snippets
Research area
The research area is Beijing, the capital city of China. The metropolis covers a total area of approximately 16,410 km2 according to statistics in 2010 by the Beijing statistics bureau. There are 35 air quality monitoring stations distributed across the entire area. These stations are classified into four categories, including 12 urban environmental evaluation sites, 16 suburban environmental evaluation sites, 5 traffic pollution monitoring sites, and 2 regional background control sites.
Since
Air pollution data
The air pollution data are acquired from 35 monitoring stations in Beijing. They are based on hourly measurements of six types of air pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) and an overall Air Quality Index (AQI) in 2016. AQI is a measure of air quality with the maximum concentration among the abovementioned six air pollutants (She et al., 2017). During data preprocessing, the hourly data are summarized to monthly averages.
Morphological characteristics
The morphological characteristics in the research area is mainly
Methods
Spatial autocorrelation and spatial heterogeneity are two fundamental characteristics of geographical data and processes. Supported by the First law of Geography (Tobler, 1970), spatial autocorrelation refers to the situation when a variable correlates with itself through space. In a general sense, spatial heterogeneity is about the uneven distribution of traits, events, relationship or spatial variation of properties across a region (Wang et al., 2016). But particularly, it concerns the
MBI
The final result of MBI is shown in Fig. 3. An MBI value can be interpreted as probability of buildings. High MBI values are found in the southeastern part of the research area. Indeed, this area of flatland is highly suitable for constructing buildings and does see high building densities.
LST and NDVI
Both LST (in Fahrenheit degree) and NDVI were calculated from the Landsat-8, Level 1 TP, OLI_TIRS imagery in the year 2016. The results are displayed in Fig. 4, Fig. 5. It can be seen from Fig. 4 that urban
Conclusions and future research directions
This study investigates the relationships between air pollution and various factors in the urban landscape including socioeconomic, urban form, and morphological characteristics. The research design combines two parallel modeling approaches, the spatiotemporal regression and social stratified heterogeneity (SSH) analysis. In the experiment, the urban morphological characteristic variable, MBI, is proven to be a statistically significant factor for the estimations of air pollution. In addition,
Acknowledgements
This work was partially supported by the State Key Laboratory of Urban and Regional Ecology, China.
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2022, Environmental PollutionCitation Excerpt :In addition, topographic conditions (Liu et al., 2018), and vegetation cover (King et al., 2014) also have contributed to the diffusion and accumulation of PM2.5. In terms of socioeconomic factors, scholars have focused on urbanization (Zhou et al., 2016; Tao et al., 2015; Wang et al., 2018b), industrial structure (Yang et al., 2018a, 2019), traffic conditions (Wang et al., 2020b; Zheng et al., 2019), anthropogenic precursors (Jing et al., 2020; Fang et al., 2021) and urban form (McCarty et al., 2015; Tian et al., 2019) to conduct research on the impact of PM2.5 concentrations, and the results show that socioeconomic factors have significant effects on PM2.5 concentrations. Several studies have attempted to identify the effect of natural and socioeconomic factors on PM2.5 concentrations (Yang et al., 2018b; Jing et al., 2020).