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The nexus between PM 2.5 and urban characteristics in the Texas triangle region
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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
The nexus between PM 2.5 and urban characteristics in the Texas triangle region Bumseok Chuna, a b c
⁎,1
, Kwangyul Choib,2, Qisheng Panc,1
Urban Planning and Environmental Policy, Texas Southern University, USA Haskayne School of Business & School of Architecture, Planning and Landscape, University of Calgary, Canada Urban Planning and Environmental Policy, Texas Southern University, USA
ABS TRA CT
Particulate matter (PM), coming from various human activities involving the burning of fuels, has many negative effects on human health. Through measurements of PM data at sparsely distributed monitoring stations across a given area, many studies have examined the simple relationship between PM and either of two urban characteristics: land cover and transportation. However, the studies of PM data from a limited number of monitoring stations have not fully accounted for variations in regional PM concentration. Furthermore, consideration of only one of two key urban characteristics may not provide a complete picture of the relationship. Hence, the primary goal of this study is to estimate the effects of both land cover and transportation on the PM 2.5 concentration indicated by satellite imagery. Focusing on the Texas Triangle region, we implemented diverse transportation measures with Geographic Information System (GIS) techniques and land cover measures with remotely sensed imagery at the census tract level. With these measures, we developed spatial regression models to examine spatially correlated effects on PM 2.5. We then used the estimated models to conduct elasticity analysis, thus helping to design an environmental policy to alleviate PM 2.5 and achieve long-term regional sustainability.
1. Introduction Today, more than half the world’s population resides in urban areas, and it is expected that 70% of the global population will live in urban environments by 2050 (United Nations, 2018). The promise of better economic activity, transport infrastructure, amenities, and other public infrastructures is sufficient to pull many people to urban districts. However, population growth within a limited space and the increased demands of urban environments contribute to increased building density with a loss of vegetation and worsening traffic congestion on the roads (Broitmann and Koomen, 2015; Chang et al., 2017). Such side effects of urban population growth accompany environmental degradation, leading not only to the emission of greenhouse gases but also to increased public health risk at national, regional, and local levels (Arrow et al., 1995). In particular, air pollution tends to have significant effects on atmospheric visibility and human health, such as increased incidence of lung cancer, lower respiratory infection, heart-related diseases, and premature death. Among air pollutants, particulate matter (PM) pollution causes visibility reduction and health problems when microscopic particles are inhaled into the human body. PM smaller than 10 µm (PM 10), similar to the size of fungal hyphae, can be absorbed into the body through a deep breath, adversely affecting medical conditions for the vulnerable sectors of the population, ranging from coughing and wheezing to asthma attacks. PM smaller than 2.5 µm (PM 2.5), close to the length of a typical bacterium, triggers diverse health risks related to cardiovascular and respiratory disease, birth defects, and chronic diseases. In addition, it decreases
⁎
Corresponding author. E-mail address: [email protected] (B. Chun). 1 Address: 3100 Cleburne St, Houston, TX 77004, USA. 2 Address: SH 300, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada. https://doi.org/10.1016/j.trd.2019.11.016 Received 12 August 2019; Received in revised form 12 November 2019; Accepted 18 November 2019 1361-9209/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Bumseok Chun, Kwangyul Choi and Qisheng Pan, Transportation Research Part D, https://doi.org/10.1016/j.trd.2019.11.016
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atmospheric visibility (U.S. Environmental Protection Agency, 2019). The harmful components of PM 2.5 originate from hundreds of different chemicals. Particularly distinct sources are particles emitted by motorized traffic systems, power plants, industries, and household fuels (Guttikunda and Calori, 2013). Poor air ventilation may affect the level of PM concentration as well, inducing isolation of air pollutants in densely built-up environments. Such urban environments contribute to worsening air quality, having a substantially negative impact on our communities. Thus, alleviating PM 2.5 pollutants is indispensable for achieving environmental sustainability at local, regional, and global levels. As of now, researchers have obtained PM data primarily from sparsely distributed monitoring stations, leaving lots of uncertainty about local PM concentration. Since such estimations at unobservable locations may induce bias, a fine-tuned analysis is necessary to properly develop regional environmental planning strategies. The goal of this study is to address the following questions, using satellite-derived data about PM 2.5 concentration: What types of land cover contribute to PM 2.5? What types of traffic information are useful for estimating PM 2.5? What are the factors strongly affecting PM 2.5 in a combination of land cover and traffic factors? To answer these questions, we obtained empirical data supporting the decision-making process by synthesizing unique features extracted from diverse spatial data sets from census tracts across the Texas Triangle region, which contains a total of 3652 census tracts. In particular, we measured the elasticities of explanatory variables with respect to PM 2.5, examining each proportional change of PM 2.5 in relation to one unit change in individual land cover or traffic factors. The answers could vary, depending on the impact of local characteristics shaped by geographic locations, climate conditions, and urban layout. However, they can offer a comprehensive outlook that can help to improve air quality over the Texas Triangle region. This study will enhance potential research directions for practical policy implementation with regard to a reduction of PM 2.5. 2. Literature review Recently, many studies have scrutinized the link between urban environments and PM 2.5. This section reviews the literature relevant to our approaches and variables to explain PM in urban environments. A large body of literature has demonstrated that PM is the dominant form of air pollution and has had negative effects on environmental quality and human health (Anderson et al., 2012; Kim et al., 2015). For this reason, there have been several attempts to understand the nexus of cause and effect for PM. Using various approaches, among which land use regression (LUR) models have been most popular, numerous studies have shown the level of PM concentration to be influenced by factors such as meteorological conditions, demographic characteristics, and land use development patterns in a given area. Previous studies have shown that meteorology (e.g., humidity, temperature, wind speed) could be the most influential factor at the macroscale due to the fact that meteorological conditions are transported (Arain et al., 2007; Li et al., 2019, 2015; Liu et al., 2009; Stafoggia et al., 2019), whereas land use patterns tend to have a significant impact on PM pollution at the microscale level (Lam and Niemeier, 2005; Yang, Chen, and Liang, 2017). In LUR models, scientists most frequently use two categories of independent variables as dominant local emission sources (land cover and transportation), and numerous studies have addressed their impact on PM concentration, demonstrating that areas associated with human activity positively affect PM 2.5 (Yang et al., 2017). Industrial areas are likely to be the most influential predictors for PM 2.5 (Hellack et al., 2017; Liu et al., 2016), and other developed areas (commercial or residential) also tend to have a positive impact on PM 2.5 (Knibbs et al., 2018). Even agricultural areas (e.g., cropland) tend to increase the level of PM 2.5 (Hu et al., 2016). However, undisturbed ecological areas show a mixed relationship. While vegetation (tree cover) and waterbodies can reduce PM 2.5 concentration through absorption and filtration capability (Hu et al., 2016; Knibbs et al., 2018; Liu et al., 2016), the impact on the PM concentration of wetland and bare land could vary, depending on the meteorological conditions of a given area (Li et al., 2019; Yang et al., 2018). Despite variations in effects by season and year, there is a consensus that transportation-related factors generally have a positive impact on the PM concentration in a given region. The intensity of roads, typically characterized by the number of kilometers of roads or the areas occupied by roads, provides the most influential transportation factor affecting the PM 2.5 concentration (Knibbs et al., 2018; Liu et al., 2016; Shi et al., 2016; Yang et al., 2017). Other transport infrastructures, such as railways and subways, also drive high PM 2.5 concentration (Hu et al., 2016). Besides that, proximity to major roads can have a significant and positive impact on PM 2.5 concentration (Lee et al., 2016). Moreover, traffic congestion and volume are the essential determinants of PM concentration, particularly in urban areas (Levy et al., 2010; Shi et al., 2016). A few studies have demonstrated the significance of traffic on PM concentrations in urban environments. Wang et al. (2017) investigated road patterns and PM 2.5 concentration connections in Beijing, China, concluding that a high-density grid-pattern road network is more likely to decrease the concentration of PM 2.5, compared with a low-density and high-grade network (e.g., an expressway). Qiu et al. (2017) studied discrepancies in PM mass concentrations among different types of roads. They found the level of PM 10 was highest on expressways among the road types they tested, including arterials, collectors, and local roads, but other PM concentrations (PM 2.5 and PM 1) showed little variance (Qiu et al., 2017). Some studies have focused on specific transportation activities. Perugu, Wei, and Yao (2016) estimated PM 2.5 associated with heavy-duty truck transportation activity in the Cincinnati urban area and found that trucks contributed to approximately three-quarters of the measurement. Early studies estimated PM concentration with information from air quality monitoring stations or by way of various modeling techniques (Gauderman et al., 2004; Jerrett et al., 2005; Künzli et al., 2005; Pope et al., 2004; Wu et al., 2006.). Accordingly, the researchers derived predictor variables (e.g., land use or traffic) by creating buffers of various sizes from those stations. However, recent studies have used satellite images to estimate air pollution and demonstrated the advantages of their higher predictive power 2
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over historical methods (Beckerman et al., 2013; Jung et al., 2018; Knibbs et al., 2018; Liu et al., 2009; van Donkelaar et al., 2010, 2019). In the LUR model, a multiple linear regression approach has been useful for understanding land use/transportation and the PM 2.5 nexus (de Hoogh et al., 2016; Olvera et al., 2012; Yang et al., 2017). However, this relatively simple statistical model may not test or fully control spatial dependency among variables, even though some studies have used advanced statistical models by integrating the LUR model with the spatiotemporal variation of both predictors and the PM concentration (Liu et al., 2009; Yu et al., 2011). As per previous studies, we examine the impact of both land cover and transportation on the formation of PM 2.5, estimated by satellite images with spatial statistics, minimizing spillover effects. This will reveal whether or not, and by how much, each explanatory variable has a different level of impact on the PM 2.5 concentration in a model. In doing so, we expect to see the perspective change on PM 2.5 concentration in the future to simulating potential planning strategies relevant to land cover and transportation.
3. Study area The Texas Triangle region encompasses 78 counties, with an area of 181,313 km2. We selected it for this study because of its noteworthy recent growth. Additionally, the region contains four megacities with populations of over one million: Austin, Dallas, Houston, and San Antonio. According to the United States Census Bureau, more than 70% of the population in the state of Texas resides in the Texas Triangle region. Moreover, researchers expect that the population of the region will grow more than 15% (≈2 million in 2019) in the next decade (Federal Reserve Bank of Dallas, 2019). This population growth will accelerate urbanization, with changes in land use/land cover and an increase of transportation infrastructure. The Texas Economic Indicators issued by the Federal Reserve Bank of Dallas in the beginning of 2019 clarified that Austin, Dallas, and San Antonio achieved an increase of over 15% in employment growth in 2015–2106, whereas the oil bust contributed to economic deterioration in Houston at the same time. However, since 2017, a 4% increase in the labor market has revitalized Houston’s economy, compared with the numbers in 2014. This economic growth has increased traffic volume and urban density in the region. According to the Texas Department of Transportation, the total daily vehicle miles traveled increased by 15.5% between 2010 and 2016 because of population growth and economic activity in the region, causing urban expansion and traffic congestion (Texas Department of Transportation, 2019). Most importantly, Dallas and Houston are notorious for being among the 20 most air-polluted cities in the United States in 2019 (American Lung Association, 2019). This evidence is sufficient to select the Texas Triangle region as the study area (see Fig. 1).
4. Data and model implementation 4.1. Data An analytic starting point requires fine-resolution census tract-level PM 2.5 data to understand the spatial pattern. To achieve this, we used satellite images to capture the air quality index. This approach helps synthesize the PM 2.5 concentration with variables, using statistical analysis and a data overlay module in GIS.
Fig. 1. Texas triangle. 3
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Fig. 2. Satellite-derived PM 2.5 and its accuracy.
4.2. PM 2.5 at the ground level Estimation of satellite-derived PM 2.5 started with the retrieval of aerosol optical depth (AOD) data from multiple satellite image sets captured by moderate resolution imaging spectroradiometer, multi-angle imaging spectro-radiometer, and sea-viewing wide field-of-view sensor, showing a reduction in the amount of solar radiation from fine particle pollution in the air. The ratio of the estimated AOD and PM 2.5 obtained from the Air Quality System operated by the U.S. Environmental Protection Agency and Environment Canada’s National Air Pollution Surveillance program helps visualize continuous spatial patterns of PM 2.5 concentration by multiplying the AOD by the ratio. Then, we adjusted the imbalance between the PM 2.5 concentration observed at monitoring stations on the ground and the PM 2.5 estimated by satellite imagery through geographically weighted regression at 1 km resolution with: 7
(Obs. PM 2.5 − Est. PM 2.5) =
∑ βi SPECi + γlog(ED)× ID log(ED) × ID i=1
(1)
where Obs. = observed at monitoring stations; Est. = estimated by satellite imagery; βi = statistical coefficients associated with each PM 2.5 component (e.g., sulfate, nitrate, ammonium, organic matters, black carbon, mineral dust, and sea salt); SPECi = the mass concentration of each component; ED = the difference between the actual elevation and the predicted elevation at a specific location; and ID = the inverse distance to the closest urban district. For details on this approach, see van Donkelaar et al. (2019) and Meng et al. (2019). Fig. 2 illustrates the spatial pattern and model accuracy of the satellite-derived PM 2.5 in 2012 at the 1 km spatial resolution used in this study. Its levels range from 6.7 μg/m3 to 12.6 μg/m3. However, four metro areas have consistently higher levels, by margins varying from 8.1 μg/m3 to 12.5 μg/m3, compared with other suburban and rural areas. Using PM 2.5 data publicly available from the Texas Commission on Environmental Quality, we tested the accuracy of satellite-derived PM 2.5 estimation. By cross-validation over the Texas Triangle region, the satellite-derived PM 2.5 concentration was statistically significant as R2 = 0.72. Such accuracy shows the potential of the application of spatial big data as an alternative to traditional methods to estimate PM 2.5 concentration. In this study, the satellite-derived PM 2.5 was a dependent variable used to navigate the effects of land cover and traffic factors on the concentration.
4.3. Land cover We employed the 2011 National Land Cover Database (NLCD) to examine the effects of land cover on the PM 2.5 concentration at the fine resolution, 30 m, of the Landsat Thematic Mapper. At the first trial for implementation of land use/land cover variables, we had considered a parcel GIS data set for the study area. However, that incurred some drawbacks pertaining to inconsistency of data structure, memory allocation during data processing, and difficulty of acquiring a complete data set over the study area. By contrast, the application of satellite images managed to reduce the problems listed above. The NLCD, implemented by an interagency consortium, provides the most timely available national land cover data set by Landsat images to support ongoing environmental analysis and policy decision-making processes (Yang et al., 2018). The U.S. Geological Survey has distributed four primary NLCD sets for the years of 1992, 2001, 2006, and 2011. The data sets are classified as eight primary land covers with 16 subclasses at the level of 30 m spatial resolution. For more NLCD information, see Yang et al. (2018). In this study, we classified seven land covers to avoid overcomplicated statistical relationships among land covers and to reveal the clear effects of individual land covers on PM 2.5. We used these classifications as explanatory variables to develop statistical models. Fig. 2 illustrates the land cover product and Table 1 provides the simple statistical summary of the new classifications and their descriptions. 4
Traffic factor
5 1.16 1.44 1.64 1.70
0.001 0.098 0.083 0.024
Road Density Distance from Interstate Distance from Principal Arterial-Other Freeways and Expressway Distance from Principal Arterial
1.29
33346.65
2.43
3.68
2.03
2.51
1.43
0.78
0.59
0.09
Coefficient of Variation
Traffic Volume
2.4
Wetland
8.1
Shrub
7.5
0.3
Forest
Barren
32.2
High Density
3.2
37.2
Low Density
Pasture
9.543
PM 2.5
PM 2.5 at the ground level Land cover
Mean
Variable
Category
Table 1 Descriptive summary of variables.
0
0 0 0
0
0
0
0
0
0
0
0
6.87
Minimum
0.46
0.0068 1.15 1.05
318,170
70
20
87
69
96
100
93
12.47
Maximum
Annual average daily traffic per census tract within the study area. It is weighted by each length of road segments within a census tract (# of vehicle). FAF road network density per census tract (m/m2) Average Euclidean distance from Interstate per census tract (Decimal Degrees) Average Euclidean distance from Principal Arterial-Other Freeways and Expressway per census tract (Decimal Degrees) Average Euclidean distance from Principal Arterial per census tract (Decimal Degrees)
Proportion of least developed land per census tract within the study area. It includes both developed-open space and developed-low intensity from NLCD (%) Proportion of densely developed land per census tract within the study area. It includes both developed-medium intensity and developed-high intensity from NLCD (%) Proportion of land covered by forest per census tract within the study area. It includes all types of forest defined by NLCD (%) Proportion of land covered by shrub per census tract within the study area. It includes all types of shrub defined by NLCD (%) Proportion of land covered by pasture/hay per census tract within the study area. It is defined by NLCD (%) Proportion of land covered by barren per census tract within the study area. It includes rock, sand, and clay defined by NLCD (%) Proportion of land covered by wetland per census tract within the study area. It includes woody wetlands and herbaceous wetland defined by NLCD (%)
Satellite-derived PM 2.5 concentration (μg/m3)
Description
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Fig. 3. Land cover, traffic volume, and road density.
4.4. Traffic factors In terms of transportation factors, we developed seven explanatory variables, including traffic volume, road density, and distance from road types defined by functional classification. First, high traffic volume increases the concentration of all traffic-related pollutants. In particular, we assume that exposure to high traffic volume increases PM 2.5 concentration levels. To test this assumption, we estimated an average traffic volume for each census tract with the following equation by using the annual average daily traffic (AADT) information as defined by the 2012 Freight Analysis Framework (FAF) Version 4: n
Traffic Volume c =
∑i = 1 (AADTi × Lic ) n
∑i = 1 Lic
(2)
where c = a specific census tract, I = road segment, L = length of road, and n = total number of road segments within a census tract. Second, we computed the road density using the 2012 FAF Version 4 to examine its effect on PM 2.5. While the impact of road density varies according to local conditions, an increase in road density tends to decrease natural resources related to sustainable environments and increase peak flows, elevating the PM 2.5 concentration. Third, we can easily observe the disparity of the PM 2.5 concentration between a location far from and near to a road. To explain this phenomenon, we calculated the average Euclidian distance from each traffic road according to its functional classification, including interstate, principal arterial-other freeways and expressway, principal arterial, minor arterial, and major collector. Then we derived an average distance to individual road types in each census tract. We expect for these variables to have a negative effect on the PM 2.5 concentration, implying that its magnitude near traffic roads is higher than its magnitude farther from them. Fig. 3 presents maps showing land cover, traffic volume, and road density over the Texas Triangle region, and Table 1 provides the simple statistical summary of the new classifications and their descriptions. Fig. 4 shows the correlation matrix as a form of heat map, helping to identify statistical incidence patterns as well as anomalies among variables. Blue represents a positive relationship among variables, and Red represents a negative relationship among variables. The stronger the color, the stronger the correlation magnitude. According to Box A, the low- and high-density variables
Fig. 4. Correlation matrix plot. *Dist 1 = Distance from Interstate; Dist 2 = Distance from Principal Arterial-Other Freeways and Expressway; Dist 3 = Distance from Principal Arterial. 6
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negatively correlated with green-related variables and distance from road variables. We also determined that traffic volume and road density had a negative association with green-related variables and distance from road variables as well, in Box B. 5. Statistical modeling The ordinary least squares (OLS) method was first used to explore the relationship between the PM 2.5 concentration and the explanatory variable X discussed previously and to obtain the best representation for statistical models, with:
PM 2.5 = α + β ·X + ε
(3)
However, the PM 2.5 level at a location observed by satellites is most likely spatially correlated with other neighboring PM 2.5 levels because it is transported, a trait highly influenced by meteorological flow characteristics, inducing spatial spillover effects. We used Moran’s I test to diagnose the spatial autocorrelation effect. If the test detected spatial autocorrelation, we adopted the spatial lag model (SAR) to estimate statistical parameters with a reduction of spatial dependency, with:
PM 2.5 = α + ρW ·PM 2.5 + β ·X + ε
(4)
where ρ is the spatial lag term for determining the magnitude of the spatial relationship between a location and its neighboring places, given the adjacency of census tracts in the GIS domain with the normalized spatial weight matrix, W, where the element wij is nonzero if census tracts i and j are directly adjacent—a situation called the first contiguity pattern—and zero otherwise. Although the introduction of the spatial lag term can make a better fit compared with R2 by OLS, it cannot completely remove the spatial effects. Perhaps spatial error models (SEMs) and general spatial models (GSMs) may also be useful to release statistical parameters from the spatial autocorrelation effect to obtain other statistical parameters of spatial modeling with better fits. However, a preliminary analysis of SEMs and GSMs boosts the R2 values higher than 0.98, implying that residuals strongly correlate to each other, and the unexplained portion of the PM 2.5 concentration should merit further discussion. Thus, we used statistical parameters estimated by SAR to conduct elasticity analysis of key variables with regard to PM 2.5 concentration. The estimation of elasticity in spatial regression models should take into account the spatial lag effect of the dependent variable PM 2.5, requiring the partial derivative of PM 2.5 with the spatial weight matrix, W. LeSage and Pace (2009) initiated a new approach reflecting spatial spillover effects based on the concept of conventional elasticity. The matrix, (I − ρW)−1, from Eq. (4) is crucial for clarifying spatial spillover effects, using direct, indirect, and total impact with regard to each explanatory variable, Xk. The direct impact quantifies the impact of the variable xik on PM 2.5 within a specific census tract i, while the effects of the variable xjk in i’s neighboring census tract j can be estimated by the indirect impact. The matrix of the partial derivative of PM 2.5 with regard to the explanatory variable Xk is:
⎡ ∂ (PM 2.5) ⋯ ∂ (PM 2.5) ⎤ = [I − ρW ]−1β k ⎢ ∂xNk ⎥ ⎦ ⎣ ∂x1k
(5)
where the diagonal elements of (5) mathematically define the direct impacts, while the off-diagonal elements define the indirect impacts. Finally, we can compute the average direct impact with:
ADIk =
1 N
N
∑ i=1
∂ (PM 2.5i) βk = ·t γ [I − ρW ]−1 ∂xik N
(6)
where t γ is the sum of diagonal elements of (5). We then estimated the average total impact with:
ATIk =
1 N
N
N
∑∑ i=1 j=1
∂ (PM 2.5i) βk ' = ·I N [I − ρW ]−1 IN ∂xjk N
(7)
where IN is the identity matrix in the dimension N. Finally, the average indirect impact is calculated as follows: (8)
AIIk =ATIk − ADIk For more detail, see LeSage and Pace (2009). 6. Results
Table 2 summarizes the statistical results across the three models as discussed in the previous section. Model 1 and Model 2 are the land cover model and the traffic model, respectively. Model 3 integrates all variables used in Models 1 and 2 to capture the overall potential effects of two categories on PM 2.5 concentration. In addition, each model contains two regression approaches, OLS and SAR, to access similarities and differences between nonspatial and spatial models. The model’s fit varies across the parameters and nonspatial/spatial models. In Model 1, while OLS led to R2 = 0.42, SAR obtained the higher R2 at 0.66. The R2 values of Model 2 with traffic-related factors are smaller than the land cover models, indicating the difficulty of the quantification of transportation impacts on PM 2.5 concentration. Such results may be led by meteorological characteristics and spatial patterns of traffic networks but need further investigation. As for Model 3, both the OLS and SAR methods had the best fits at 0.48 and 0.68, respectively. All the SAR models reduce spatial dependency and show a better performance than the OLS models. We retain the SAR models for further analyses and discussion. 7
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Table 2 Summary of statistical results.
Category Land cover
Variable
Model 2: Traffic-related factor
Model 3: Land cover + Traffic-related factor
OLS
OLS
SAR
OLS
SAR
0.11 (1.57) 1.33*** (20.58) −2.21*** (−13.14) 0.34*** (3.42) 1.26 (1.19) 2.45*** (12.45)
0.01 (1.15) 0.03*** (2.89) −0.06* (−1.94) 0.05*** (2.88) −0.03 (−0.16) 0.08** (2.43)
SAR
0.45 (6.42) 1.65*** (26.39) −3.19*** (−18.99) 0.01 (0.11) 3.37*** (3.03) 2.51*** (12.07)
0.03 (2.59) 0.05*** (4.50) −0.10*** (−3.52) 0.03* (1.89) 0.06 (0.174) 0.08** (2.26)
–
–
–
–
–
–
–
–
–
–
–
–
Traffic Volume
–
–
Road Density
–
–
Distance from Interstate
–
–
Distance from Principal Arterial-Other Freeways and Expressway Distance from Principal Arterial
–
–
−9.742 × 10−7 (−0.399) 96.19*** (4.56) −1.58*** (−16.60) −2.20*** (−17.99)
2.401 × 10−7*** (4.61) 3.89*** (16,745.39) −0.049*** (−3.42) −0.08*** (−4.26)
−6.52 × 10−6 (−1.59) −28.75 (−1.47) −1.27*** (−14.40) −1.18*** (−10.06)
1.87 × 10−7*** (3.52) 2.45*** (14,389.06) −0.05*** (−3.48) −0.07*** (−3.49)
–
–
−1.67*** (−4.20) 9.86*** (451.52) –
−0.013 (−0.22) 0.11*** (8.07) 0.989*** (679.87) 0.47 3652
−0.62* (−1.66) 9.32*** (170.24) –
−0.02 (−0.34) 0.13*** (13.76) 0.98*** (673.51) 0.68 3652
Low Density High Density Shrub Pasture Barren Wetland
Traffic-related factor
Model 1: Land cover
Const.
***
***
Spatial lag term, ρ
8.87 (171.07) –
R2 # of Samples
0.42 3652
**
***
0.08 (19.58) 0.989*** (937.74) 0.66 3652
0.37 3652
0.48 3652
() = Asymptotic t-statistics. *** P < 0.01. ** P < 0.05. * < 0.1.
In Model 1, we examined the effects of six land covers on PM 2.5 concentration. Both low density and high density tend to increase the level of PM 2.5 concentration, implying that built environments are likely to enhance the concentration within the Texas Triangle region. However, statistical coefficients indicate that land cover with high density has a stronger effect, compared with low density. This may partly be due to the obstruction of air ventilation by buildings in densely built areas. As expected, the proportion of land with shrubs has a negative association with the level of PM 2.5 concentration. However, we identified a positive effect of pasture on the PM 2.5 mass concentration, different from our initial expectation. According to reports by Ceccarelli and Smit (2017), like traffic-related pollution, long-term exposure to barn dust in a pasture can cause chronic respiratory conditions due to the impact of large amounts of dust particles from straw, manure, and livestock, contributing to the generation of secondary inorganic aerosols, which can support the positive effect of pasture on PM 2.5 concentration. A greater proportion of wetland also tends to increase the level of PM 2.5 across the Texas Triangle region, implying that PM 2.5 concentration positively correlates to humidity. Recently, Yang et al. (2018) also found a positive effect of a wetland environment on PM 2.5 concentration. Their findings were supported by meteorological conditions; temperature and wind speed, for example, played a role in dust deposition. For more information relevant to the effects of temperature and wind speed, see Yang et al. (2018). The effects of barren land cover on PM 2.5 concentration were uncertain. In Model 2, most traffic-related factors were statistically significant at 99%, excluding distances from the two types of roads classified as principal arterial and major collector. First, this supports the idea that both traffic volume and road density contribute to enhancing the level of PM 2.5 concentration. Second, Model 2 clarified a disparity between locations far from and near to a road with a negative statistical coefficient, indicating that PM 2.5 concentration elevates near traffic road segments. In addition, it shows that highway traffic on interstate and principal arterial-other freeways and expressways contributes to local ambient PM 2.5 concentration. With an exception of the coefficient for low density, most of the statistical parameters in Model 3 were similar to the outcomes in Model 1 and Model 2. Rather than discussing statistical coefficients in this model, we further conduct descriptive and spatial elasticity analysis of key variables with regard to PM 2.5 concentration in the following section. Fig. 5 employs stacked bar charts to summarize elasticity values, with a combination of direct impact at a specific location in red
8
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Fig. 5. Summary of elasticity analysis for key variables.
and indirect impact coming from neighboring places in green, for the only statistical key variables. Relatively, all direct impacts are much smaller than indirect impacts, implying that neighboring patterns of land cover and traffic characteristics intensively affect the level of PM 2.5 concentration in a specific area. This strongly supports the idea that spatial concepts should be considered to lower the PM 2.5 concentration level. Each stacked bar represents the total impact of each variable. Fig. 5 implies that we should prioritize retrofitting high-density areas with environmentally friendly strategies to deal with the air pollution issues. According to the elasticity analysis, a 1% increase in high-density areas would enhance PM 2.5 concentration intensity by 0% to 0.149%, depending upon local characteristics. For four fast-growing cities within the Texas Triangle, we can expect that the concentration of PM 2.5 keeps elevating over time. Pasture and wetland also bring positive elasticity effects, varying from 0% to 0.303% and from 0% to nearly 0.313%, respectively. Conversely, shrub coverage can reduce the PM 2.5 concentration level by 0.25% with 1% shrub increase. Interestingly, the elasticities of both traffic volume and road density are smaller with ranges from 0% to 0.03% for traffic volume and from 0% to 0.08% for road density, as compared with key land cover variables, although we expect that these traffic-related variables are strongly associated with PM 2.5. This may be due to the effect of pollution dispersion by aerodynamics from traffic. This assumption requires further research for clarification. As for the distance variables sorted by functional classification, this study reveals the clear patterns of road distance impact on PM 2.5 concentration from the average elasticity. Locations far away from minor arterials have a tendency to amplify the PM 2.5 concentration reduction effect. By contrast, the average elasticities, −0.27 and −0.34, of the interstate and principal arterials-other freeway and expressway are less than the minor arterial elasticity, −0.44. The result is likely to support retrofitting local environmental policies near interstate and principal arterials. 7. Discussion and policy implications This study proposed using satellite-derived PM 2.5 to explore its concentration with land cover data and transportation factors over the Texas Triangle region, a relatively large area. However, we posed some significant issues related to methodology, including more measurements by ground monitoring stations, unexpected land cover impact, and local urban geometric characteristics. First, it was necessary to validate the relationship between satellite-derived PM 2.5 and the in-situ level of PM 2.5 in suburban and rural areas. As discussed in the section on data, there are 17 monitoring stations measuring PM 2.5 over the Texas Triangle region. However, only one was placed outside of city boundaries, implying a difficulty in PM 2.5 concentration observation measured by monitoring stations, which can be a key limitation of site-specific data in rural areas. Most air cleaning programs focus on metropolitan regions. For community or neighborhood planning purposes, the operation of density monitoring stations in urban districts is effective. Meanwhile, we also need to account for spatial patterns of air pollution to understand its moving patterns concretely. Our findings from Model 1 and Model 2 support the assertion that pasture contributes to raising the level of PM 2.5. This indicates a need for continuous monitoring stations, to reveal uneven PM 2.5 concentration in rural areas. In reality, it is impossible to install air pollution monitoring stations regularly all over the place. How do we set them up to properly reflect local PM 2.5 concentration or other pollutants? Homogenous land cover patterns in rural areas may require a relatively small number of stations to evaluate air quality, compared with urban districts covered with heterogeneous surface materials. In the case of Texas, most rural areas are dominantly covered by natural resources with less topological variation, producing simple patterns of air pollutant levels. 9
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Second, our statistical models raise additional questions relevant to unexpected land cover impact on the PM 2.5 level in rural areas, especially pasture and wetlands. As we mentioned in the results section, the study by Yang et al. (2018) demonstrated a positive correlation between relative humidity and particulate matter. However, these results are likely to produce controversy because of complex meteorological characteristics, such as temperature, wind speed and direction, and precipitation. Lou et al. (2017) revealed that the amount of humidity could affect PM 2.5 concentration. For example, higher relative humidity reduces PM 2.5 concentration. However, low relative humidity enhances the concentration, implying that it probably varies depending upon local characteristics. Third, our findings indicate that high density plays a key role in PM 2.5 concentration. How can we adopt this logic to implement a clean air program in urban districts using statistical models, especially in metropolitan regions? Building geometry and traffic volume are the key relevant features in metropolitan districts. Planners and researchers have explored ways to reduce diverse air pollutants in the face of urban expansion. However, few studies have investigated urban geometry in a three-dimensional (3-D) domain, so as to enhance air ventilation effects and reduce air pollutants. That might be another empirical way to implement sustainable urban environments. If necessary data were available, we could test the air ventilation effect to reduce the PM 2.5 concentration level, using an alternative index, the Sky View Factor. For this research, we might need to narrow the scope to a specific urban district and examine each city separately, rather than the entire Texas Triangle at once, because of memory allocation issues relating to the weak power of both numeric computation and data processing. In so doing, we could explore the effects of urban geometry, land cover configuration, and traffic patterns in detail. As a sequence of this study, we will add 3-D urban characteristics to the existing approach, thereby achieving better statistical analysis and potentially reducing PM 2.5 concentration in the future. Fourth, as for satellite-derived PM 2.5 concentration, different meteorological factors may affect the relationship between AOD and PM 2.5 in the process of parameterization. This is a limitation of using satellite imagery to estimate PM 2.5 concentration. Heterogeneous geographical locations may need different parameters to estimate AOD and PM 2.5 concentration due to many different factors, such as wind flow, precipitation, moisture, and temperature. It is hard to represent all the types of meteorological parameters covering the entire Texas Triangle region. In particular, the number of weather stations is not sufficient for the study area. Properly reflecting local weather characteristics may become more complicated. Despite the limitations of this study and the necessity for further investigation, the study findings could have several policy implications for the Texas Triangle region. High-density development brings numerous environmental benefits by making good use of limited resources. However, our findings suggest that high-density areas require landscaping with vegetation that functions properly to reduce the PM concentration in the area. In addition, a series of accurate simulations of building geometry prior to construction may be necessary for the better understanding of its impact on the air quality of an area. In terms of transportation, it is obvious that the more traffic there is, the worse the air quality will be. The most sustainable way to deal with the pollution from passenger travel would be reducing the use of private cars through multimodal planning and supportive land use planning. At the same time, it would be critical to predict the synergistic impact of land cover and traffic on PM 2.5 concentration. 8. Conclusion This study examined the impacts of land cover and traffic factors on PM 2.5 concentration with spatial regression models over the Texas Triangle. We used satellite images to estimate PM 2.5 concentration by AOD, then computed averages for each census tract. The satellite-derived PM 2.5 concentration helped us easily understand the spatial patterns of its concentration without missing values at specific locations. In addition, it contributed to the synthesis of heterogeneous land cover and traffic factors in regard to PM 2.5 concentration, thereby allowing for the development of statistical models to parameterize the explanatory variables at the census tract level. To summarize our findings, it is worth noting that the distance to road variables tend to strongly influence PM 2.5 concentration. In line with our initial expectations, the closer that locations were to traffic networks, the more intensive the PM 2.5 concentration. Interestingly, we found that the impact of land cover was stronger than other key traffic factors, such as traffic volume and road density. High density tends to increase the PM 2.5 level, possibly accounting for a reduction of air ventilation effects. Further analysis should be conducted to unveil the effects of densely built areas in a 3-D domain. Pasture and wetlands also had some positive effects on PM 2.5. In contrast, shrubs contributed to reduce its concentration. Lastly, this study showed the spatial interaction of the PM 2.5 concentration observed by the average direct, indirect, and total impact, so as to explain the relative magnitude of the explanatory variables’ impact on the PM concentration level across the region. Acknowledgments This work was supported by the Cooperative Mobility for Competitive Megaregions (CM2), USDOT University Transportation Center (UTC) (Grant numbers: #CM2-25 and #CM2-49, 2018-2019). We thank Dr. Aaron van Donkelaar, research associate, and Dr. Randall Martin, professor in the Atmospheric Composition Analysis Group at Dalhousie University, for assistance with the satellite-derived PM 2.5 images. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.trd.2019.11.016. 10
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References American Lung Association, 2019. State of the air 2019: 20th Anniversary. https://www.lung.org/assets/documents/healthy-air/state-of-the-air/sota-2019-full.pdf. Anderson, J.O., Thundiyil, J.G., Stolbach, A., 2012. Clearing the air: a review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175. https://doi.org/10.1007/s13181-011-0203-1. Arain, M.A., Blair, R., Finkelstein, N., Brook, J.R., Sahsuvaroglu, T., Beckerman, B., et al., 2007. The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies. Atmos. Environ. 41, 3453–3464. https://doi.org/10.1016/j.atmosenv.2006.11.063. Arrow, K., Bolin, B., Costanza, R., Dasgupta, P., Folke, C., Holling, C., Jansson, B.-O., Levin, S., Mäleri, K.-G., Perrings, C., Pimentel, D., 1995. Economic growth, carrying capacity, and the environment. Science 268 (5210), 520–521. https://doi.org/10.1126/science.268.5210.520. Beckerman, B.S., Jerrett, M., Serre, M., Martin, R.V., Lee, S.J., Van Donkelaar, A., et al., 2013. A hybrid approach to estimating national scale spatiotemporal variability of PM 2.5 in the contiguous United States. Environ. Sci. Technol. 47, 7233–7241. https://doi.org/10.1021/es400039u. Broitmann, D., Koomen, E., 2015. Residential density change: Densification and urban expansion. Comput. Environ. Urban Syst. 54, 32–46. https://doi.org/10.1016/j. compenvurbsys.2015.05.006. Ceccarelli, D., Smit, L., 2017. Is living near a farm bad for your health? Eos 98, 2017. https://doi.org/10.1029/2018EO082985. Published on 28 September. Chang, Y., Lee, Y., Choi, S., 2017. Is there more traffic congestion in larger cities? Scaling analysis of the 101 largest U.S. urban centers. Transp. Policy 59, 54–63. https://doi.org/ 10.1016/j.tranpol.2017.07.002. de Hoogh, K., Gulliver, J., van Donkelaar, A., Martin, R.V., Marshall, J.D., Bechle, M.J., et al., 2016. Development of west-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data. Environ. Res. 151, 1–10. https://doi.org/10.1016/j.envres.2016.07.005. Department of Economic and Social Affairs, United Nations, 2018. Revision of world urbanization prospects. https://www.un.org/development/desa/publications/2018revision-of-world-urbanization-prospects.html. Federal Reserve Bank of Dallas, 2019. Texas Economic Indicators. https://www.dallasfed.org/research/indicators/tei.aspx. Gauderman, W.J., Avol, E., Gilliland, F., Vora, H., Thomas, D., Berhane, K., et al., 2004. The effect of air pollution on lung development from 10 to 18 years of age. N. Engl. J. Med. 351 (11), 1057–1067. Guttikunda, S., Calori, G., 2013. A GIS based emissions inventory at 1km × 1 km spatial resolution for air pollution analysis in Delhi, India. Atmos. Environ. 67, 101–111. https:// doi.org/10.1016/j.atmosenv.2012.10.040. Hellack, B., Sugiri, D., Schins, R.P.F., Schikowski, T., Krämer, U., Kuhlbusch, T.A.J., Hoffmann, B., 2017. Land use regression modeling of oxidative potential of fine particles, NO2, PM2.5 mass and association to type two diabetes mellitus. Atmos. Environ. 171, 181–190. https://doi.org/10.1016/j.atmosenv.2017.10.017. Hu, L., Liu, J., He, Z., 2016. Self-adaptive revised land use regression models for estimating PM2.5 concentrations in Beijing, China. Sustainability (Switzerland) 8, 786. https:// doi.org/10.3390/su8080786. Jerrett, M., Burnett, R.T., Ma, R., Pope, C.A., Krewski, D., Newbold, K.B., et al., 2005. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology 16 (6), 727–736. https://doi.org/10.1097/01.ede.0000181630.15826.7d. Jung, C.-R., Hwang, B.-F., Chen, W.-T., 2018. Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM 2.5 concentrations in Taiwan from 2005 to 2015. Environ. Pollut. 237, 1000–1010. https://doi.org/10.1016/j.envpol.2017.11.016. Kim, K.-H., Kabir, E., Kabir, S., 2015. A review on the human health impact of airborne particulate matter. Environ. Int. 74, 136–143. https://doi.org/10.1016/j.envint.2014.10. 005. Knibbs, L.D., Van Donkelaar, A., Martin, R.V., Bechle, M.J., Brauer, M., Cohen, D.D., et al., 2018. Satellite-based land-use regression for continental-scale long-term ambient PM 2.5 exposure assessment in Australia. Environ. Sci. Technol. 52, 12445–12455. https://doi.org/10.1021/acs.est.8b02328. Künzli, N., Jerrett, M., Mack, W.J., Beckerman, B., LaBree, L., Gilliland, F., et al., 2005. Ambient air pollution and atherosclerosis in Los Angeles. Environ. Health Perspect. 113 (2), 201–206. https://doi.org/10.1289/ehp.7523. Lam, T., Niemeier, D., 2005. An exploratory study of the impact of common land-use policies on air quality. Transp. Res. Part D: Transp. Environ. 10, 365–383. https://doi.org/ 10.1016/j.trd.2005.04.011. Lee, H.J., Chatfield, R.B., Strawa, A.W., 2016. Enhancing the applicability of satellite remote sensing for PM2.5 estimation using MODIS deep blue AOD and land use regression in California, United States. Environ. Sci. Technol. 50, 6546–6555. https://doi.org/10.1021/acs.est.6b01438. LeSage, J., Pace, R.K., 2009. Introduction to Spatial Econometrics. Chapman and Hall/CRC. Levy, J.I., Buonocore, J.J., von Stackelberg, K., 2010. Evaluation of the public health impacts of traffic congestion: a health risk assessment. Environ. Health 9 (1), 65. Li, C., Huang, Y., Guo, H., Wu, G., Wang, Y., Li, W., Cui, L., 2019. The concentrations and removal effects of PM10 and PM2.5 on a wetland in Beijing. Sustainability 11, 1312. https://doi.org/10.3390/su11051312. Li, X., Liu, W., Chen, Z., Zeng, G., Hu, C., León, T., et al., 2015. The application of semicircular-buffer-based land use regression models incorporating wind direction in predicting quarterly NO2 and PM10 concentrations. Atmos. Environ. 103, 18–24. https://doi.org/10.1016/j.atmosenv.2014.12.004. Liu, C., Henderson, B.H., Wang, D., Yang, X., Peng, Z., 2016. A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China. Sci. Total Environ. 565, 607–615. https://doi.org/10.1016/j.scitotenv.2016.03.189. Liu, Y., Paciorek, C.J., Koutrakis, P., 2009. Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information. Environ. Health Perspect. 117, 886–892. https://doi.org/10.1289/ehp.0800123. Meng, J., Li, C., Martin, R.V., van Donkelaar, A., Hystad, P., Brauer, M., 2019. Estimated long-term (1981–2016) concentrations of ambient fine particulate matter across North America from chemical transport modeling, satellite remote sensing, and ground-based measurements. Environ. Sci. Technol. 53, 5071–5079. https://doi.org/10.1021/acs. est.8b06875. Olvera, H.A., Garcia, M., Li, W.-W., Yang, H., Amaya, M.A., Myers, O., et al., 2012. Principal component analysis optimization of a PM2.5 land use regression model with small monitoring network. Sci. Total Environ. 425, 27–34. https://doi.org/10.1016/j.scitotenv.2012.02.068. Perugu, H., Wei, H., Yao, Z., 2016. Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area. Transp. Res. Part D: Transp. Environ. 46, 114–127. https://doi.org/10.1016/j.trd.2016.03.013. Pope, C.A., Burnett, R.T., Thurston, G.D., Thun, M.J., Calle, E.E., Krewski, D., Godleski, J.J., 2004. Cardiovascular mortality and long-term exposure to particulate air pollution: epidemiological evidence of general pathophysiological pathways of disease. Circulation 109, 71–77. https://doi.org/10.1161/01.cir.0000108927.80044.7f. Qiu, Z., Xu, X., Song, J., Luo, Y., Zhao, R., Xiang, B., et al., 2017. Pedestrian exposure to traffic PM on different types of urban roads: a case study of Xi’an, China. Sustain. Cities Soc. 32, 475–485. https://doi.org/10.1016/j.scs.2017.04.007. Shi, Y., Lau, K.K.-L., Ng, E., 2016. Developing street-level PM 2.5 and PM 10 land use regression models in high-density Hong Kong with urban morphological factors. Environ. Sci. Technol. 50, 8178–8187. https://doi.org/10.1021/acs.est.6b01807. Stafoggia, M., Bellander, T., Bucci, S., Davoli, M., de Hoogh, K., de’ Donato, F., et al., 2019. Estimation of daily PM 10 and PM 2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model. Environ. Int. 124, 170–179. https://doi.org/10.1016/j.envint.2019.01.016. Texas Department of Transportation, 2019. Road Inventory. https://www.txdot.gov/inside-txdot/division/transportation-planning/roadway-inventory.html. U.S. Environmental Protection Agency, 2019. Particulate Matter (PM) Pollution. https://www.epa.gov/pm-pollution. van Donkelaar, A., Martin, R.V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., Villeneuve, P.J., 2010. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118, 847–855. https://doi.org/10.1289/ehp.0901623. van Donkelaar, A., Martin, R.V., Li, C., Burnett, R.T., 2019. Regional estimates of chemical composition of fine particulate matter using a combined geoscience-statistical method with information from satellites, models, and monitors. Environ. Sci. Technol. 53, 2595–2611. https://doi.org/10.1021/acs.est.8b06392. research-article. Wang, F., Peng, Y., Jiang, C., 2017. Influence of road patterns on PM2.5 concentrations and the available solutions: the case of Beijing City, China. Sustainability 9, 217. https:// doi.org/10.3390/su9020217. Wu, J., Winer, A.M., Delfino, R.J., 2006. Exposure assessment of particulate matter air pollution before, during, and after the 2003 Southern California wildfires. Atmos. Environ. 40, 3333–3348. https://doi.org/10.1016/j.atmosenv.2006.01.056. Yang, H., Chen, W., Liang, Z., 2017. Impact of land use on PM2.5 pollution in a representative city of middle China. Int. J. Environ. Res. Public Health 14, 462. https://doi.org/10. 3390/ijerph14050462. Yang, T., Wang, Y., Wu, Y., Zhai, J., Cong, L., Yan, G., Zhang, Z., Li, C., 2018. Effect of the wetland environment on particulate matter and dry deposition. Environ. Technol. 1–11. https://doi.org/10.1080/09593330.2018.1520307. Yu, H.-L., Wang, C.-H., Liu, M.-C., Kuo, Y.-M., 2011. Estimation of fine particulate matter in Taipei using landuse regression and Bayesian maximum entropy methods. Int. J. Environ. Res. Public Health 8, 2153–2169. https://doi.org/10.3390/ijerph8062153.
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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
The nexus between PM 2.5 and urban characteristics in the Texas triangle region Bumseok Chuna, a b c
⁎,1
, Kwangyul Choib,2, Qisheng Panc,1
Urban Planning and Environmental Policy, Texas Southern University, USA Haskayne School of Business & School of Architecture, Planning and Landscape, University of Calgary, Canada Urban Planning and Environmental Policy, Texas Southern University, USA
ABS TRA CT
Particulate matter (PM), coming from various human activities involving the burning of fuels, has many negative effects on human health. Through measurements of PM data at sparsely distributed monitoring stations across a given area, many studies have examined the simple relationship between PM and either of two urban characteristics: land cover and transportation. However, the studies of PM data from a limited number of monitoring stations have not fully accounted for variations in regional PM concentration. Furthermore, consideration of only one of two key urban characteristics may not provide a complete picture of the relationship. Hence, the primary goal of this study is to estimate the effects of both land cover and transportation on the PM 2.5 concentration indicated by satellite imagery. Focusing on the Texas Triangle region, we implemented diverse transportation measures with Geographic Information System (GIS) techniques and land cover measures with remotely sensed imagery at the census tract level. With these measures, we developed spatial regression models to examine spatially correlated effects on PM 2.5. We then used the estimated models to conduct elasticity analysis, thus helping to design an environmental policy to alleviate PM 2.5 and achieve long-term regional sustainability.
1. Introduction Today, more than half the world’s population resides in urban areas, and it is expected that 70% of the global population will live in urban environments by 2050 (United Nations, 2018). The promise of better economic activity, transport infrastructure, amenities, and other public infrastructures is sufficient to pull many people to urban districts. However, population growth within a limited space and the increased demands of urban environments contribute to increased building density with a loss of vegetation and worsening traffic congestion on the roads (Broitmann and Koomen, 2015; Chang et al., 2017). Such side effects of urban population growth accompany environmental degradation, leading not only to the emission of greenhouse gases but also to increased public health risk at national, regional, and local levels (Arrow et al., 1995). In particular, air pollution tends to have significant effects on atmospheric visibility and human health, such as increased incidence of lung cancer, lower respiratory infection, heart-related diseases, and premature death. Among air pollutants, particulate matter (PM) pollution causes visibility reduction and health problems when microscopic particles are inhaled into the human body. PM smaller than 10 µm (PM 10), similar to the size of fungal hyphae, can be absorbed into the body through a deep breath, adversely affecting medical conditions for the vulnerable sectors of the population, ranging from coughing and wheezing to asthma attacks. PM smaller than 2.5 µm (PM 2.5), close to the length of a typical bacterium, triggers diverse health risks related to cardiovascular and respiratory disease, birth defects, and chronic diseases. In addition, it decreases
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Corresponding author. E-mail address: [email protected] (B. Chun). 1 Address: 3100 Cleburne St, Houston, TX 77004, USA. 2 Address: SH 300, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada. https://doi.org/10.1016/j.trd.2019.11.016 Received 12 August 2019; Received in revised form 12 November 2019; Accepted 18 November 2019 1361-9209/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: Bumseok Chun, Kwangyul Choi and Qisheng Pan, Transportation Research Part D, https://doi.org/10.1016/j.trd.2019.11.016
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atmospheric visibility (U.S. Environmental Protection Agency, 2019). The harmful components of PM 2.5 originate from hundreds of different chemicals. Particularly distinct sources are particles emitted by motorized traffic systems, power plants, industries, and household fuels (Guttikunda and Calori, 2013). Poor air ventilation may affect the level of PM concentration as well, inducing isolation of air pollutants in densely built-up environments. Such urban environments contribute to worsening air quality, having a substantially negative impact on our communities. Thus, alleviating PM 2.5 pollutants is indispensable for achieving environmental sustainability at local, regional, and global levels. As of now, researchers have obtained PM data primarily from sparsely distributed monitoring stations, leaving lots of uncertainty about local PM concentration. Since such estimations at unobservable locations may induce bias, a fine-tuned analysis is necessary to properly develop regional environmental planning strategies. The goal of this study is to address the following questions, using satellite-derived data about PM 2.5 concentration: What types of land cover contribute to PM 2.5? What types of traffic information are useful for estimating PM 2.5? What are the factors strongly affecting PM 2.5 in a combination of land cover and traffic factors? To answer these questions, we obtained empirical data supporting the decision-making process by synthesizing unique features extracted from diverse spatial data sets from census tracts across the Texas Triangle region, which contains a total of 3652 census tracts. In particular, we measured the elasticities of explanatory variables with respect to PM 2.5, examining each proportional change of PM 2.5 in relation to one unit change in individual land cover or traffic factors. The answers could vary, depending on the impact of local characteristics shaped by geographic locations, climate conditions, and urban layout. However, they can offer a comprehensive outlook that can help to improve air quality over the Texas Triangle region. This study will enhance potential research directions for practical policy implementation with regard to a reduction of PM 2.5. 2. Literature review Recently, many studies have scrutinized the link between urban environments and PM 2.5. This section reviews the literature relevant to our approaches and variables to explain PM in urban environments. A large body of literature has demonstrated that PM is the dominant form of air pollution and has had negative effects on environmental quality and human health (Anderson et al., 2012; Kim et al., 2015). For this reason, there have been several attempts to understand the nexus of cause and effect for PM. Using various approaches, among which land use regression (LUR) models have been most popular, numerous studies have shown the level of PM concentration to be influenced by factors such as meteorological conditions, demographic characteristics, and land use development patterns in a given area. Previous studies have shown that meteorology (e.g., humidity, temperature, wind speed) could be the most influential factor at the macroscale due to the fact that meteorological conditions are transported (Arain et al., 2007; Li et al., 2019, 2015; Liu et al., 2009; Stafoggia et al., 2019), whereas land use patterns tend to have a significant impact on PM pollution at the microscale level (Lam and Niemeier, 2005; Yang, Chen, and Liang, 2017). In LUR models, scientists most frequently use two categories of independent variables as dominant local emission sources (land cover and transportation), and numerous studies have addressed their impact on PM concentration, demonstrating that areas associated with human activity positively affect PM 2.5 (Yang et al., 2017). Industrial areas are likely to be the most influential predictors for PM 2.5 (Hellack et al., 2017; Liu et al., 2016), and other developed areas (commercial or residential) also tend to have a positive impact on PM 2.5 (Knibbs et al., 2018). Even agricultural areas (e.g., cropland) tend to increase the level of PM 2.5 (Hu et al., 2016). However, undisturbed ecological areas show a mixed relationship. While vegetation (tree cover) and waterbodies can reduce PM 2.5 concentration through absorption and filtration capability (Hu et al., 2016; Knibbs et al., 2018; Liu et al., 2016), the impact on the PM concentration of wetland and bare land could vary, depending on the meteorological conditions of a given area (Li et al., 2019; Yang et al., 2018). Despite variations in effects by season and year, there is a consensus that transportation-related factors generally have a positive impact on the PM concentration in a given region. The intensity of roads, typically characterized by the number of kilometers of roads or the areas occupied by roads, provides the most influential transportation factor affecting the PM 2.5 concentration (Knibbs et al., 2018; Liu et al., 2016; Shi et al., 2016; Yang et al., 2017). Other transport infrastructures, such as railways and subways, also drive high PM 2.5 concentration (Hu et al., 2016). Besides that, proximity to major roads can have a significant and positive impact on PM 2.5 concentration (Lee et al., 2016). Moreover, traffic congestion and volume are the essential determinants of PM concentration, particularly in urban areas (Levy et al., 2010; Shi et al., 2016). A few studies have demonstrated the significance of traffic on PM concentrations in urban environments. Wang et al. (2017) investigated road patterns and PM 2.5 concentration connections in Beijing, China, concluding that a high-density grid-pattern road network is more likely to decrease the concentration of PM 2.5, compared with a low-density and high-grade network (e.g., an expressway). Qiu et al. (2017) studied discrepancies in PM mass concentrations among different types of roads. They found the level of PM 10 was highest on expressways among the road types they tested, including arterials, collectors, and local roads, but other PM concentrations (PM 2.5 and PM 1) showed little variance (Qiu et al., 2017). Some studies have focused on specific transportation activities. Perugu, Wei, and Yao (2016) estimated PM 2.5 associated with heavy-duty truck transportation activity in the Cincinnati urban area and found that trucks contributed to approximately three-quarters of the measurement. Early studies estimated PM concentration with information from air quality monitoring stations or by way of various modeling techniques (Gauderman et al., 2004; Jerrett et al., 2005; Künzli et al., 2005; Pope et al., 2004; Wu et al., 2006.). Accordingly, the researchers derived predictor variables (e.g., land use or traffic) by creating buffers of various sizes from those stations. However, recent studies have used satellite images to estimate air pollution and demonstrated the advantages of their higher predictive power 2
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over historical methods (Beckerman et al., 2013; Jung et al., 2018; Knibbs et al., 2018; Liu et al., 2009; van Donkelaar et al., 2010, 2019). In the LUR model, a multiple linear regression approach has been useful for understanding land use/transportation and the PM 2.5 nexus (de Hoogh et al., 2016; Olvera et al., 2012; Yang et al., 2017). However, this relatively simple statistical model may not test or fully control spatial dependency among variables, even though some studies have used advanced statistical models by integrating the LUR model with the spatiotemporal variation of both predictors and the PM concentration (Liu et al., 2009; Yu et al., 2011). As per previous studies, we examine the impact of both land cover and transportation on the formation of PM 2.5, estimated by satellite images with spatial statistics, minimizing spillover effects. This will reveal whether or not, and by how much, each explanatory variable has a different level of impact on the PM 2.5 concentration in a model. In doing so, we expect to see the perspective change on PM 2.5 concentration in the future to simulating potential planning strategies relevant to land cover and transportation.
3. Study area The Texas Triangle region encompasses 78 counties, with an area of 181,313 km2. We selected it for this study because of its noteworthy recent growth. Additionally, the region contains four megacities with populations of over one million: Austin, Dallas, Houston, and San Antonio. According to the United States Census Bureau, more than 70% of the population in the state of Texas resides in the Texas Triangle region. Moreover, researchers expect that the population of the region will grow more than 15% (≈2 million in 2019) in the next decade (Federal Reserve Bank of Dallas, 2019). This population growth will accelerate urbanization, with changes in land use/land cover and an increase of transportation infrastructure. The Texas Economic Indicators issued by the Federal Reserve Bank of Dallas in the beginning of 2019 clarified that Austin, Dallas, and San Antonio achieved an increase of over 15% in employment growth in 2015–2106, whereas the oil bust contributed to economic deterioration in Houston at the same time. However, since 2017, a 4% increase in the labor market has revitalized Houston’s economy, compared with the numbers in 2014. This economic growth has increased traffic volume and urban density in the region. According to the Texas Department of Transportation, the total daily vehicle miles traveled increased by 15.5% between 2010 and 2016 because of population growth and economic activity in the region, causing urban expansion and traffic congestion (Texas Department of Transportation, 2019). Most importantly, Dallas and Houston are notorious for being among the 20 most air-polluted cities in the United States in 2019 (American Lung Association, 2019). This evidence is sufficient to select the Texas Triangle region as the study area (see Fig. 1).
4. Data and model implementation 4.1. Data An analytic starting point requires fine-resolution census tract-level PM 2.5 data to understand the spatial pattern. To achieve this, we used satellite images to capture the air quality index. This approach helps synthesize the PM 2.5 concentration with variables, using statistical analysis and a data overlay module in GIS.
Fig. 1. Texas triangle. 3
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Fig. 2. Satellite-derived PM 2.5 and its accuracy.
4.2. PM 2.5 at the ground level Estimation of satellite-derived PM 2.5 started with the retrieval of aerosol optical depth (AOD) data from multiple satellite image sets captured by moderate resolution imaging spectroradiometer, multi-angle imaging spectro-radiometer, and sea-viewing wide field-of-view sensor, showing a reduction in the amount of solar radiation from fine particle pollution in the air. The ratio of the estimated AOD and PM 2.5 obtained from the Air Quality System operated by the U.S. Environmental Protection Agency and Environment Canada’s National Air Pollution Surveillance program helps visualize continuous spatial patterns of PM 2.5 concentration by multiplying the AOD by the ratio. Then, we adjusted the imbalance between the PM 2.5 concentration observed at monitoring stations on the ground and the PM 2.5 estimated by satellite imagery through geographically weighted regression at 1 km resolution with: 7
(Obs. PM 2.5 − Est. PM 2.5) =
∑ βi SPECi + γlog(ED)× ID log(ED) × ID i=1
(1)
where Obs. = observed at monitoring stations; Est. = estimated by satellite imagery; βi = statistical coefficients associated with each PM 2.5 component (e.g., sulfate, nitrate, ammonium, organic matters, black carbon, mineral dust, and sea salt); SPECi = the mass concentration of each component; ED = the difference between the actual elevation and the predicted elevation at a specific location; and ID = the inverse distance to the closest urban district. For details on this approach, see van Donkelaar et al. (2019) and Meng et al. (2019). Fig. 2 illustrates the spatial pattern and model accuracy of the satellite-derived PM 2.5 in 2012 at the 1 km spatial resolution used in this study. Its levels range from 6.7 μg/m3 to 12.6 μg/m3. However, four metro areas have consistently higher levels, by margins varying from 8.1 μg/m3 to 12.5 μg/m3, compared with other suburban and rural areas. Using PM 2.5 data publicly available from the Texas Commission on Environmental Quality, we tested the accuracy of satellite-derived PM 2.5 estimation. By cross-validation over the Texas Triangle region, the satellite-derived PM 2.5 concentration was statistically significant as R2 = 0.72. Such accuracy shows the potential of the application of spatial big data as an alternative to traditional methods to estimate PM 2.5 concentration. In this study, the satellite-derived PM 2.5 was a dependent variable used to navigate the effects of land cover and traffic factors on the concentration.
4.3. Land cover We employed the 2011 National Land Cover Database (NLCD) to examine the effects of land cover on the PM 2.5 concentration at the fine resolution, 30 m, of the Landsat Thematic Mapper. At the first trial for implementation of land use/land cover variables, we had considered a parcel GIS data set for the study area. However, that incurred some drawbacks pertaining to inconsistency of data structure, memory allocation during data processing, and difficulty of acquiring a complete data set over the study area. By contrast, the application of satellite images managed to reduce the problems listed above. The NLCD, implemented by an interagency consortium, provides the most timely available national land cover data set by Landsat images to support ongoing environmental analysis and policy decision-making processes (Yang et al., 2018). The U.S. Geological Survey has distributed four primary NLCD sets for the years of 1992, 2001, 2006, and 2011. The data sets are classified as eight primary land covers with 16 subclasses at the level of 30 m spatial resolution. For more NLCD information, see Yang et al. (2018). In this study, we classified seven land covers to avoid overcomplicated statistical relationships among land covers and to reveal the clear effects of individual land covers on PM 2.5. We used these classifications as explanatory variables to develop statistical models. Fig. 2 illustrates the land cover product and Table 1 provides the simple statistical summary of the new classifications and their descriptions. 4
Traffic factor
5 1.16 1.44 1.64 1.70
0.001 0.098 0.083 0.024
Road Density Distance from Interstate Distance from Principal Arterial-Other Freeways and Expressway Distance from Principal Arterial
1.29
33346.65
2.43
3.68
2.03
2.51
1.43
0.78
0.59
0.09
Coefficient of Variation
Traffic Volume
2.4
Wetland
8.1
Shrub
7.5
0.3
Forest
Barren
32.2
High Density
3.2
37.2
Low Density
Pasture
9.543
PM 2.5
PM 2.5 at the ground level Land cover
Mean
Variable
Category
Table 1 Descriptive summary of variables.
0
0 0 0
0
0
0
0
0
0
0
0
6.87
Minimum
0.46
0.0068 1.15 1.05
318,170
70
20
87
69
96
100
93
12.47
Maximum
Annual average daily traffic per census tract within the study area. It is weighted by each length of road segments within a census tract (# of vehicle). FAF road network density per census tract (m/m2) Average Euclidean distance from Interstate per census tract (Decimal Degrees) Average Euclidean distance from Principal Arterial-Other Freeways and Expressway per census tract (Decimal Degrees) Average Euclidean distance from Principal Arterial per census tract (Decimal Degrees)
Proportion of least developed land per census tract within the study area. It includes both developed-open space and developed-low intensity from NLCD (%) Proportion of densely developed land per census tract within the study area. It includes both developed-medium intensity and developed-high intensity from NLCD (%) Proportion of land covered by forest per census tract within the study area. It includes all types of forest defined by NLCD (%) Proportion of land covered by shrub per census tract within the study area. It includes all types of shrub defined by NLCD (%) Proportion of land covered by pasture/hay per census tract within the study area. It is defined by NLCD (%) Proportion of land covered by barren per census tract within the study area. It includes rock, sand, and clay defined by NLCD (%) Proportion of land covered by wetland per census tract within the study area. It includes woody wetlands and herbaceous wetland defined by NLCD (%)
Satellite-derived PM 2.5 concentration (μg/m3)
Description
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Fig. 3. Land cover, traffic volume, and road density.
4.4. Traffic factors In terms of transportation factors, we developed seven explanatory variables, including traffic volume, road density, and distance from road types defined by functional classification. First, high traffic volume increases the concentration of all traffic-related pollutants. In particular, we assume that exposure to high traffic volume increases PM 2.5 concentration levels. To test this assumption, we estimated an average traffic volume for each census tract with the following equation by using the annual average daily traffic (AADT) information as defined by the 2012 Freight Analysis Framework (FAF) Version 4: n
Traffic Volume c =
∑i = 1 (AADTi × Lic ) n
∑i = 1 Lic
(2)
where c = a specific census tract, I = road segment, L = length of road, and n = total number of road segments within a census tract. Second, we computed the road density using the 2012 FAF Version 4 to examine its effect on PM 2.5. While the impact of road density varies according to local conditions, an increase in road density tends to decrease natural resources related to sustainable environments and increase peak flows, elevating the PM 2.5 concentration. Third, we can easily observe the disparity of the PM 2.5 concentration between a location far from and near to a road. To explain this phenomenon, we calculated the average Euclidian distance from each traffic road according to its functional classification, including interstate, principal arterial-other freeways and expressway, principal arterial, minor arterial, and major collector. Then we derived an average distance to individual road types in each census tract. We expect for these variables to have a negative effect on the PM 2.5 concentration, implying that its magnitude near traffic roads is higher than its magnitude farther from them. Fig. 3 presents maps showing land cover, traffic volume, and road density over the Texas Triangle region, and Table 1 provides the simple statistical summary of the new classifications and their descriptions. Fig. 4 shows the correlation matrix as a form of heat map, helping to identify statistical incidence patterns as well as anomalies among variables. Blue represents a positive relationship among variables, and Red represents a negative relationship among variables. The stronger the color, the stronger the correlation magnitude. According to Box A, the low- and high-density variables
Fig. 4. Correlation matrix plot. *Dist 1 = Distance from Interstate; Dist 2 = Distance from Principal Arterial-Other Freeways and Expressway; Dist 3 = Distance from Principal Arterial. 6
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negatively correlated with green-related variables and distance from road variables. We also determined that traffic volume and road density had a negative association with green-related variables and distance from road variables as well, in Box B. 5. Statistical modeling The ordinary least squares (OLS) method was first used to explore the relationship between the PM 2.5 concentration and the explanatory variable X discussed previously and to obtain the best representation for statistical models, with:
PM 2.5 = α + β ·X + ε
(3)
However, the PM 2.5 level at a location observed by satellites is most likely spatially correlated with other neighboring PM 2.5 levels because it is transported, a trait highly influenced by meteorological flow characteristics, inducing spatial spillover effects. We used Moran’s I test to diagnose the spatial autocorrelation effect. If the test detected spatial autocorrelation, we adopted the spatial lag model (SAR) to estimate statistical parameters with a reduction of spatial dependency, with:
PM 2.5 = α + ρW ·PM 2.5 + β ·X + ε
(4)
where ρ is the spatial lag term for determining the magnitude of the spatial relationship between a location and its neighboring places, given the adjacency of census tracts in the GIS domain with the normalized spatial weight matrix, W, where the element wij is nonzero if census tracts i and j are directly adjacent—a situation called the first contiguity pattern—and zero otherwise. Although the introduction of the spatial lag term can make a better fit compared with R2 by OLS, it cannot completely remove the spatial effects. Perhaps spatial error models (SEMs) and general spatial models (GSMs) may also be useful to release statistical parameters from the spatial autocorrelation effect to obtain other statistical parameters of spatial modeling with better fits. However, a preliminary analysis of SEMs and GSMs boosts the R2 values higher than 0.98, implying that residuals strongly correlate to each other, and the unexplained portion of the PM 2.5 concentration should merit further discussion. Thus, we used statistical parameters estimated by SAR to conduct elasticity analysis of key variables with regard to PM 2.5 concentration. The estimation of elasticity in spatial regression models should take into account the spatial lag effect of the dependent variable PM 2.5, requiring the partial derivative of PM 2.5 with the spatial weight matrix, W. LeSage and Pace (2009) initiated a new approach reflecting spatial spillover effects based on the concept of conventional elasticity. The matrix, (I − ρW)−1, from Eq. (4) is crucial for clarifying spatial spillover effects, using direct, indirect, and total impact with regard to each explanatory variable, Xk. The direct impact quantifies the impact of the variable xik on PM 2.5 within a specific census tract i, while the effects of the variable xjk in i’s neighboring census tract j can be estimated by the indirect impact. The matrix of the partial derivative of PM 2.5 with regard to the explanatory variable Xk is:
⎡ ∂ (PM 2.5) ⋯ ∂ (PM 2.5) ⎤ = [I − ρW ]−1β k ⎢ ∂xNk ⎥ ⎦ ⎣ ∂x1k
(5)
where the diagonal elements of (5) mathematically define the direct impacts, while the off-diagonal elements define the indirect impacts. Finally, we can compute the average direct impact with:
ADIk =
1 N
N
∑ i=1
∂ (PM 2.5i) βk = ·t γ [I − ρW ]−1 ∂xik N
(6)
where t γ is the sum of diagonal elements of (5). We then estimated the average total impact with:
ATIk =
1 N
N
N
∑∑ i=1 j=1
∂ (PM 2.5i) βk ' = ·I N [I − ρW ]−1 IN ∂xjk N
(7)
where IN is the identity matrix in the dimension N. Finally, the average indirect impact is calculated as follows: (8)
AIIk =ATIk − ADIk For more detail, see LeSage and Pace (2009). 6. Results
Table 2 summarizes the statistical results across the three models as discussed in the previous section. Model 1 and Model 2 are the land cover model and the traffic model, respectively. Model 3 integrates all variables used in Models 1 and 2 to capture the overall potential effects of two categories on PM 2.5 concentration. In addition, each model contains two regression approaches, OLS and SAR, to access similarities and differences between nonspatial and spatial models. The model’s fit varies across the parameters and nonspatial/spatial models. In Model 1, while OLS led to R2 = 0.42, SAR obtained the higher R2 at 0.66. The R2 values of Model 2 with traffic-related factors are smaller than the land cover models, indicating the difficulty of the quantification of transportation impacts on PM 2.5 concentration. Such results may be led by meteorological characteristics and spatial patterns of traffic networks but need further investigation. As for Model 3, both the OLS and SAR methods had the best fits at 0.48 and 0.68, respectively. All the SAR models reduce spatial dependency and show a better performance than the OLS models. We retain the SAR models for further analyses and discussion. 7
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Table 2 Summary of statistical results.
Category Land cover
Variable
Model 2: Traffic-related factor
Model 3: Land cover + Traffic-related factor
OLS
OLS
SAR
OLS
SAR
0.11 (1.57) 1.33*** (20.58) −2.21*** (−13.14) 0.34*** (3.42) 1.26 (1.19) 2.45*** (12.45)
0.01 (1.15) 0.03*** (2.89) −0.06* (−1.94) 0.05*** (2.88) −0.03 (−0.16) 0.08** (2.43)
SAR
0.45 (6.42) 1.65*** (26.39) −3.19*** (−18.99) 0.01 (0.11) 3.37*** (3.03) 2.51*** (12.07)
0.03 (2.59) 0.05*** (4.50) −0.10*** (−3.52) 0.03* (1.89) 0.06 (0.174) 0.08** (2.26)
–
–
–
–
–
–
–
–
–
–
–
–
Traffic Volume
–
–
Road Density
–
–
Distance from Interstate
–
–
Distance from Principal Arterial-Other Freeways and Expressway Distance from Principal Arterial
–
–
−9.742 × 10−7 (−0.399) 96.19*** (4.56) −1.58*** (−16.60) −2.20*** (−17.99)
2.401 × 10−7*** (4.61) 3.89*** (16,745.39) −0.049*** (−3.42) −0.08*** (−4.26)
−6.52 × 10−6 (−1.59) −28.75 (−1.47) −1.27*** (−14.40) −1.18*** (−10.06)
1.87 × 10−7*** (3.52) 2.45*** (14,389.06) −0.05*** (−3.48) −0.07*** (−3.49)
–
–
−1.67*** (−4.20) 9.86*** (451.52) –
−0.013 (−0.22) 0.11*** (8.07) 0.989*** (679.87) 0.47 3652
−0.62* (−1.66) 9.32*** (170.24) –
−0.02 (−0.34) 0.13*** (13.76) 0.98*** (673.51) 0.68 3652
Low Density High Density Shrub Pasture Barren Wetland
Traffic-related factor
Model 1: Land cover
Const.
***
***
Spatial lag term, ρ
8.87 (171.07) –
R2 # of Samples
0.42 3652
**
***
0.08 (19.58) 0.989*** (937.74) 0.66 3652
0.37 3652
0.48 3652
() = Asymptotic t-statistics. *** P < 0.01. ** P < 0.05. * < 0.1.
In Model 1, we examined the effects of six land covers on PM 2.5 concentration. Both low density and high density tend to increase the level of PM 2.5 concentration, implying that built environments are likely to enhance the concentration within the Texas Triangle region. However, statistical coefficients indicate that land cover with high density has a stronger effect, compared with low density. This may partly be due to the obstruction of air ventilation by buildings in densely built areas. As expected, the proportion of land with shrubs has a negative association with the level of PM 2.5 concentration. However, we identified a positive effect of pasture on the PM 2.5 mass concentration, different from our initial expectation. According to reports by Ceccarelli and Smit (2017), like traffic-related pollution, long-term exposure to barn dust in a pasture can cause chronic respiratory conditions due to the impact of large amounts of dust particles from straw, manure, and livestock, contributing to the generation of secondary inorganic aerosols, which can support the positive effect of pasture on PM 2.5 concentration. A greater proportion of wetland also tends to increase the level of PM 2.5 across the Texas Triangle region, implying that PM 2.5 concentration positively correlates to humidity. Recently, Yang et al. (2018) also found a positive effect of a wetland environment on PM 2.5 concentration. Their findings were supported by meteorological conditions; temperature and wind speed, for example, played a role in dust deposition. For more information relevant to the effects of temperature and wind speed, see Yang et al. (2018). The effects of barren land cover on PM 2.5 concentration were uncertain. In Model 2, most traffic-related factors were statistically significant at 99%, excluding distances from the two types of roads classified as principal arterial and major collector. First, this supports the idea that both traffic volume and road density contribute to enhancing the level of PM 2.5 concentration. Second, Model 2 clarified a disparity between locations far from and near to a road with a negative statistical coefficient, indicating that PM 2.5 concentration elevates near traffic road segments. In addition, it shows that highway traffic on interstate and principal arterial-other freeways and expressways contributes to local ambient PM 2.5 concentration. With an exception of the coefficient for low density, most of the statistical parameters in Model 3 were similar to the outcomes in Model 1 and Model 2. Rather than discussing statistical coefficients in this model, we further conduct descriptive and spatial elasticity analysis of key variables with regard to PM 2.5 concentration in the following section. Fig. 5 employs stacked bar charts to summarize elasticity values, with a combination of direct impact at a specific location in red
8
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Fig. 5. Summary of elasticity analysis for key variables.
and indirect impact coming from neighboring places in green, for the only statistical key variables. Relatively, all direct impacts are much smaller than indirect impacts, implying that neighboring patterns of land cover and traffic characteristics intensively affect the level of PM 2.5 concentration in a specific area. This strongly supports the idea that spatial concepts should be considered to lower the PM 2.5 concentration level. Each stacked bar represents the total impact of each variable. Fig. 5 implies that we should prioritize retrofitting high-density areas with environmentally friendly strategies to deal with the air pollution issues. According to the elasticity analysis, a 1% increase in high-density areas would enhance PM 2.5 concentration intensity by 0% to 0.149%, depending upon local characteristics. For four fast-growing cities within the Texas Triangle, we can expect that the concentration of PM 2.5 keeps elevating over time. Pasture and wetland also bring positive elasticity effects, varying from 0% to 0.303% and from 0% to nearly 0.313%, respectively. Conversely, shrub coverage can reduce the PM 2.5 concentration level by 0.25% with 1% shrub increase. Interestingly, the elasticities of both traffic volume and road density are smaller with ranges from 0% to 0.03% for traffic volume and from 0% to 0.08% for road density, as compared with key land cover variables, although we expect that these traffic-related variables are strongly associated with PM 2.5. This may be due to the effect of pollution dispersion by aerodynamics from traffic. This assumption requires further research for clarification. As for the distance variables sorted by functional classification, this study reveals the clear patterns of road distance impact on PM 2.5 concentration from the average elasticity. Locations far away from minor arterials have a tendency to amplify the PM 2.5 concentration reduction effect. By contrast, the average elasticities, −0.27 and −0.34, of the interstate and principal arterials-other freeway and expressway are less than the minor arterial elasticity, −0.44. The result is likely to support retrofitting local environmental policies near interstate and principal arterials. 7. Discussion and policy implications This study proposed using satellite-derived PM 2.5 to explore its concentration with land cover data and transportation factors over the Texas Triangle region, a relatively large area. However, we posed some significant issues related to methodology, including more measurements by ground monitoring stations, unexpected land cover impact, and local urban geometric characteristics. First, it was necessary to validate the relationship between satellite-derived PM 2.5 and the in-situ level of PM 2.5 in suburban and rural areas. As discussed in the section on data, there are 17 monitoring stations measuring PM 2.5 over the Texas Triangle region. However, only one was placed outside of city boundaries, implying a difficulty in PM 2.5 concentration observation measured by monitoring stations, which can be a key limitation of site-specific data in rural areas. Most air cleaning programs focus on metropolitan regions. For community or neighborhood planning purposes, the operation of density monitoring stations in urban districts is effective. Meanwhile, we also need to account for spatial patterns of air pollution to understand its moving patterns concretely. Our findings from Model 1 and Model 2 support the assertion that pasture contributes to raising the level of PM 2.5. This indicates a need for continuous monitoring stations, to reveal uneven PM 2.5 concentration in rural areas. In reality, it is impossible to install air pollution monitoring stations regularly all over the place. How do we set them up to properly reflect local PM 2.5 concentration or other pollutants? Homogenous land cover patterns in rural areas may require a relatively small number of stations to evaluate air quality, compared with urban districts covered with heterogeneous surface materials. In the case of Texas, most rural areas are dominantly covered by natural resources with less topological variation, producing simple patterns of air pollutant levels. 9
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Second, our statistical models raise additional questions relevant to unexpected land cover impact on the PM 2.5 level in rural areas, especially pasture and wetlands. As we mentioned in the results section, the study by Yang et al. (2018) demonstrated a positive correlation between relative humidity and particulate matter. However, these results are likely to produce controversy because of complex meteorological characteristics, such as temperature, wind speed and direction, and precipitation. Lou et al. (2017) revealed that the amount of humidity could affect PM 2.5 concentration. For example, higher relative humidity reduces PM 2.5 concentration. However, low relative humidity enhances the concentration, implying that it probably varies depending upon local characteristics. Third, our findings indicate that high density plays a key role in PM 2.5 concentration. How can we adopt this logic to implement a clean air program in urban districts using statistical models, especially in metropolitan regions? Building geometry and traffic volume are the key relevant features in metropolitan districts. Planners and researchers have explored ways to reduce diverse air pollutants in the face of urban expansion. However, few studies have investigated urban geometry in a three-dimensional (3-D) domain, so as to enhance air ventilation effects and reduce air pollutants. That might be another empirical way to implement sustainable urban environments. If necessary data were available, we could test the air ventilation effect to reduce the PM 2.5 concentration level, using an alternative index, the Sky View Factor. For this research, we might need to narrow the scope to a specific urban district and examine each city separately, rather than the entire Texas Triangle at once, because of memory allocation issues relating to the weak power of both numeric computation and data processing. In so doing, we could explore the effects of urban geometry, land cover configuration, and traffic patterns in detail. As a sequence of this study, we will add 3-D urban characteristics to the existing approach, thereby achieving better statistical analysis and potentially reducing PM 2.5 concentration in the future. Fourth, as for satellite-derived PM 2.5 concentration, different meteorological factors may affect the relationship between AOD and PM 2.5 in the process of parameterization. This is a limitation of using satellite imagery to estimate PM 2.5 concentration. Heterogeneous geographical locations may need different parameters to estimate AOD and PM 2.5 concentration due to many different factors, such as wind flow, precipitation, moisture, and temperature. It is hard to represent all the types of meteorological parameters covering the entire Texas Triangle region. In particular, the number of weather stations is not sufficient for the study area. Properly reflecting local weather characteristics may become more complicated. Despite the limitations of this study and the necessity for further investigation, the study findings could have several policy implications for the Texas Triangle region. High-density development brings numerous environmental benefits by making good use of limited resources. However, our findings suggest that high-density areas require landscaping with vegetation that functions properly to reduce the PM concentration in the area. In addition, a series of accurate simulations of building geometry prior to construction may be necessary for the better understanding of its impact on the air quality of an area. In terms of transportation, it is obvious that the more traffic there is, the worse the air quality will be. The most sustainable way to deal with the pollution from passenger travel would be reducing the use of private cars through multimodal planning and supportive land use planning. At the same time, it would be critical to predict the synergistic impact of land cover and traffic on PM 2.5 concentration. 8. Conclusion This study examined the impacts of land cover and traffic factors on PM 2.5 concentration with spatial regression models over the Texas Triangle. We used satellite images to estimate PM 2.5 concentration by AOD, then computed averages for each census tract. The satellite-derived PM 2.5 concentration helped us easily understand the spatial patterns of its concentration without missing values at specific locations. In addition, it contributed to the synthesis of heterogeneous land cover and traffic factors in regard to PM 2.5 concentration, thereby allowing for the development of statistical models to parameterize the explanatory variables at the census tract level. To summarize our findings, it is worth noting that the distance to road variables tend to strongly influence PM 2.5 concentration. In line with our initial expectations, the closer that locations were to traffic networks, the more intensive the PM 2.5 concentration. Interestingly, we found that the impact of land cover was stronger than other key traffic factors, such as traffic volume and road density. High density tends to increase the PM 2.5 level, possibly accounting for a reduction of air ventilation effects. Further analysis should be conducted to unveil the effects of densely built areas in a 3-D domain. Pasture and wetlands also had some positive effects on PM 2.5. In contrast, shrubs contributed to reduce its concentration. Lastly, this study showed the spatial interaction of the PM 2.5 concentration observed by the average direct, indirect, and total impact, so as to explain the relative magnitude of the explanatory variables’ impact on the PM concentration level across the region. Acknowledgments This work was supported by the Cooperative Mobility for Competitive Megaregions (CM2), USDOT University Transportation Center (UTC) (Grant numbers: #CM2-25 and #CM2-49, 2018-2019). We thank Dr. Aaron van Donkelaar, research associate, and Dr. Randall Martin, professor in the Atmospheric Composition Analysis Group at Dalhousie University, for assistance with the satellite-derived PM 2.5 images. Appendix A. Supplementary material Supplementary data to this article can be found online at https://doi.org/10.1016/j.trd.2019.11.016. 10
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References American Lung Association, 2019. State of the air 2019: 20th Anniversary. https://www.lung.org/assets/documents/healthy-air/state-of-the-air/sota-2019-full.pdf. Anderson, J.O., Thundiyil, J.G., Stolbach, A., 2012. Clearing the air: a review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175. https://doi.org/10.1007/s13181-011-0203-1. Arain, M.A., Blair, R., Finkelstein, N., Brook, J.R., Sahsuvaroglu, T., Beckerman, B., et al., 2007. The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies. Atmos. Environ. 41, 3453–3464. https://doi.org/10.1016/j.atmosenv.2006.11.063. Arrow, K., Bolin, B., Costanza, R., Dasgupta, P., Folke, C., Holling, C., Jansson, B.-O., Levin, S., Mäleri, K.-G., Perrings, C., Pimentel, D., 1995. Economic growth, carrying capacity, and the environment. Science 268 (5210), 520–521. https://doi.org/10.1126/science.268.5210.520. Beckerman, B.S., Jerrett, M., Serre, M., Martin, R.V., Lee, S.J., Van Donkelaar, A., et al., 2013. A hybrid approach to estimating national scale spatiotemporal variability of PM 2.5 in the contiguous United States. Environ. Sci. Technol. 47, 7233–7241. https://doi.org/10.1021/es400039u. Broitmann, D., Koomen, E., 2015. Residential density change: Densification and urban expansion. Comput. Environ. Urban Syst. 54, 32–46. https://doi.org/10.1016/j. compenvurbsys.2015.05.006. Ceccarelli, D., Smit, L., 2017. Is living near a farm bad for your health? Eos 98, 2017. https://doi.org/10.1029/2018EO082985. Published on 28 September. Chang, Y., Lee, Y., Choi, S., 2017. Is there more traffic congestion in larger cities? Scaling analysis of the 101 largest U.S. urban centers. Transp. Policy 59, 54–63. https://doi.org/ 10.1016/j.tranpol.2017.07.002. de Hoogh, K., Gulliver, J., van Donkelaar, A., Martin, R.V., Marshall, J.D., Bechle, M.J., et al., 2016. Development of west-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data. Environ. Res. 151, 1–10. https://doi.org/10.1016/j.envres.2016.07.005. Department of Economic and Social Affairs, United Nations, 2018. Revision of world urbanization prospects. https://www.un.org/development/desa/publications/2018revision-of-world-urbanization-prospects.html. Federal Reserve Bank of Dallas, 2019. Texas Economic Indicators. https://www.dallasfed.org/research/indicators/tei.aspx. Gauderman, W.J., Avol, E., Gilliland, F., Vora, H., Thomas, D., Berhane, K., et al., 2004. The effect of air pollution on lung development from 10 to 18 years of age. N. Engl. J. Med. 351 (11), 1057–1067. Guttikunda, S., Calori, G., 2013. A GIS based emissions inventory at 1km × 1 km spatial resolution for air pollution analysis in Delhi, India. Atmos. Environ. 67, 101–111. https:// doi.org/10.1016/j.atmosenv.2012.10.040. Hellack, B., Sugiri, D., Schins, R.P.F., Schikowski, T., Krämer, U., Kuhlbusch, T.A.J., Hoffmann, B., 2017. Land use regression modeling of oxidative potential of fine particles, NO2, PM2.5 mass and association to type two diabetes mellitus. Atmos. Environ. 171, 181–190. https://doi.org/10.1016/j.atmosenv.2017.10.017. Hu, L., Liu, J., He, Z., 2016. Self-adaptive revised land use regression models for estimating PM2.5 concentrations in Beijing, China. Sustainability (Switzerland) 8, 786. https:// doi.org/10.3390/su8080786. Jerrett, M., Burnett, R.T., Ma, R., Pope, C.A., Krewski, D., Newbold, K.B., et al., 2005. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology 16 (6), 727–736. https://doi.org/10.1097/01.ede.0000181630.15826.7d. Jung, C.-R., Hwang, B.-F., Chen, W.-T., 2018. Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM 2.5 concentrations in Taiwan from 2005 to 2015. Environ. Pollut. 237, 1000–1010. https://doi.org/10.1016/j.envpol.2017.11.016. Kim, K.-H., Kabir, E., Kabir, S., 2015. A review on the human health impact of airborne particulate matter. Environ. Int. 74, 136–143. https://doi.org/10.1016/j.envint.2014.10. 005. Knibbs, L.D., Van Donkelaar, A., Martin, R.V., Bechle, M.J., Brauer, M., Cohen, D.D., et al., 2018. Satellite-based land-use regression for continental-scale long-term ambient PM 2.5 exposure assessment in Australia. Environ. Sci. Technol. 52, 12445–12455. https://doi.org/10.1021/acs.est.8b02328. Künzli, N., Jerrett, M., Mack, W.J., Beckerman, B., LaBree, L., Gilliland, F., et al., 2005. Ambient air pollution and atherosclerosis in Los Angeles. Environ. Health Perspect. 113 (2), 201–206. https://doi.org/10.1289/ehp.7523. Lam, T., Niemeier, D., 2005. An exploratory study of the impact of common land-use policies on air quality. Transp. Res. Part D: Transp. Environ. 10, 365–383. https://doi.org/ 10.1016/j.trd.2005.04.011. Lee, H.J., Chatfield, R.B., Strawa, A.W., 2016. Enhancing the applicability of satellite remote sensing for PM2.5 estimation using MODIS deep blue AOD and land use regression in California, United States. Environ. Sci. Technol. 50, 6546–6555. https://doi.org/10.1021/acs.est.6b01438. LeSage, J., Pace, R.K., 2009. Introduction to Spatial Econometrics. Chapman and Hall/CRC. Levy, J.I., Buonocore, J.J., von Stackelberg, K., 2010. Evaluation of the public health impacts of traffic congestion: a health risk assessment. Environ. Health 9 (1), 65. Li, C., Huang, Y., Guo, H., Wu, G., Wang, Y., Li, W., Cui, L., 2019. The concentrations and removal effects of PM10 and PM2.5 on a wetland in Beijing. Sustainability 11, 1312. https://doi.org/10.3390/su11051312. Li, X., Liu, W., Chen, Z., Zeng, G., Hu, C., León, T., et al., 2015. The application of semicircular-buffer-based land use regression models incorporating wind direction in predicting quarterly NO2 and PM10 concentrations. Atmos. Environ. 103, 18–24. https://doi.org/10.1016/j.atmosenv.2014.12.004. Liu, C., Henderson, B.H., Wang, D., Yang, X., Peng, Z., 2016. A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China. Sci. Total Environ. 565, 607–615. https://doi.org/10.1016/j.scitotenv.2016.03.189. Liu, Y., Paciorek, C.J., Koutrakis, P., 2009. Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information. Environ. Health Perspect. 117, 886–892. https://doi.org/10.1289/ehp.0800123. Meng, J., Li, C., Martin, R.V., van Donkelaar, A., Hystad, P., Brauer, M., 2019. Estimated long-term (1981–2016) concentrations of ambient fine particulate matter across North America from chemical transport modeling, satellite remote sensing, and ground-based measurements. Environ. Sci. Technol. 53, 5071–5079. https://doi.org/10.1021/acs. est.8b06875. Olvera, H.A., Garcia, M., Li, W.-W., Yang, H., Amaya, M.A., Myers, O., et al., 2012. Principal component analysis optimization of a PM2.5 land use regression model with small monitoring network. Sci. Total Environ. 425, 27–34. https://doi.org/10.1016/j.scitotenv.2012.02.068. Perugu, H., Wei, H., Yao, Z., 2016. Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area. Transp. Res. Part D: Transp. Environ. 46, 114–127. https://doi.org/10.1016/j.trd.2016.03.013. Pope, C.A., Burnett, R.T., Thurston, G.D., Thun, M.J., Calle, E.E., Krewski, D., Godleski, J.J., 2004. Cardiovascular mortality and long-term exposure to particulate air pollution: epidemiological evidence of general pathophysiological pathways of disease. Circulation 109, 71–77. https://doi.org/10.1161/01.cir.0000108927.80044.7f. Qiu, Z., Xu, X., Song, J., Luo, Y., Zhao, R., Xiang, B., et al., 2017. Pedestrian exposure to traffic PM on different types of urban roads: a case study of Xi’an, China. Sustain. Cities Soc. 32, 475–485. https://doi.org/10.1016/j.scs.2017.04.007. Shi, Y., Lau, K.K.-L., Ng, E., 2016. Developing street-level PM 2.5 and PM 10 land use regression models in high-density Hong Kong with urban morphological factors. Environ. Sci. Technol. 50, 8178–8187. https://doi.org/10.1021/acs.est.6b01807. Stafoggia, M., Bellander, T., Bucci, S., Davoli, M., de Hoogh, K., de’ Donato, F., et al., 2019. Estimation of daily PM 10 and PM 2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model. Environ. Int. 124, 170–179. https://doi.org/10.1016/j.envint.2019.01.016. Texas Department of Transportation, 2019. Road Inventory. https://www.txdot.gov/inside-txdot/division/transportation-planning/roadway-inventory.html. U.S. Environmental Protection Agency, 2019. Particulate Matter (PM) Pollution. https://www.epa.gov/pm-pollution. van Donkelaar, A., Martin, R.V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., Villeneuve, P.J., 2010. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Perspect. 118, 847–855. https://doi.org/10.1289/ehp.0901623. van Donkelaar, A., Martin, R.V., Li, C., Burnett, R.T., 2019. Regional estimates of chemical composition of fine particulate matter using a combined geoscience-statistical method with information from satellites, models, and monitors. Environ. Sci. Technol. 53, 2595–2611. https://doi.org/10.1021/acs.est.8b06392. research-article. Wang, F., Peng, Y., Jiang, C., 2017. Influence of road patterns on PM2.5 concentrations and the available solutions: the case of Beijing City, China. Sustainability 9, 217. https:// doi.org/10.3390/su9020217. Wu, J., Winer, A.M., Delfino, R.J., 2006. Exposure assessment of particulate matter air pollution before, during, and after the 2003 Southern California wildfires. Atmos. Environ. 40, 3333–3348. https://doi.org/10.1016/j.atmosenv.2006.01.056. Yang, H., Chen, W., Liang, Z., 2017. Impact of land use on PM2.5 pollution in a representative city of middle China. Int. J. Environ. Res. Public Health 14, 462. https://doi.org/10. 3390/ijerph14050462. Yang, T., Wang, Y., Wu, Y., Zhai, J., Cong, L., Yan, G., Zhang, Z., Li, C., 2018. Effect of the wetland environment on particulate matter and dry deposition. Environ. Technol. 1–11. https://doi.org/10.1080/09593330.2018.1520307. Yu, H.-L., Wang, C.-H., Liu, M.-C., Kuo, Y.-M., 2011. Estimation of fine particulate matter in Taipei using landuse regression and Bayesian maximum entropy methods. Int. J. Environ. Res. Public Health 8, 2153–2169. https://doi.org/10.3390/ijerph8062153.
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