Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China
<p>Distribution of the monitoring sites in Beijing. Different symbols represent different types of monitoring sites.</p> "> Figure 2
<p>The average PM<sub>2.5</sub> concentrations at different monitoring sites. Circles of the same size but different colors represent different PM<sub>2.5</sub> concentration ranges.</p> "> Figure 3
<p>Flow chart for the distribution of environmental pollutant concentrations based on the typical method with improvements in the self-adaptive revised LUR model (brown background).</p> "> Figure 4
<p>Even sampling points in the target area (1191 points are shown in the Beijing area).</p> "> Figure 5
<p>The distribution of PM<sub>2.5</sub> in the winter of 2014 in Beijing based on the self-adaptive revised LUR model and spatial interpolation.</p> "> Figure 6
<p>Profile line from north to south in Beijing.</p> "> Figure 7
<p>PM<sub>2.5</sub> concentration variation profile for winter 2014 in Beijing.</p> "> Figure 8
<p>The error distribution map of the self-adaptive revised LUR model at different monitoring sites. Circles of the same size but with different colors represent different error ranges.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. PM2.5 Data and Predictor Variables
2.2.1. Environmental Pollutant Monitoring Data for PM2.5
2.2.2. Predictor Variables
- (1)
- Land use. Land use data for Beijing were primarily obtained from the latest national census dataset of year 2012 and were classified according to the Chinese National Land Use Classification Standard [46]. After extracting the land use variables for Beijing, it was observed that the main types of land use were farmland, forest land, garden plots, urban land, water, grassland, and roadways. Due to the small roadways and grassland areas, these two types of land use were not considered in this paper. Thus, five predictor variables were used for land class at the area of Beijing: farmland x1, forest land x2, garden plots x3, urban land x4, and water x5.
- (2)
- Terrain. The terrain of Beijing consists of mountains in the northwest and plains in the southeast. Thus, the terrain is more complex in the northwest region of Beijing and less complex in the southeast. Terrain data were obtained from the ASTER GDEM with a resolution of 30 m. For the terrain class, two predictor variables were selected: the average elevation, x6, and the average slope in degrees, x7.
- (3)
- Transportation. The transportation lines in Beijing are more densely located in the center of the city than in the suburbs and include fine street lines x8, railways x9 and water lines x10. Transportation data were obtained from Open Street Map [47], which is an open-source resource.
- (4)
- Population. Demographic data were obtained from the Beijing 2014 statistical yearbook [48]. Based on the different administrative units, the population x11 distribution was used as the main statistic.
- (5)
- Polluting enterprises. Enterprises that release pollutants to the environment are important factors for estimating the environmental pollutant concentrations. Numbers and locations of the polluting enterprises have been obtained from the National Administration for Code Allocation to Organization, and the data were collected in 2012. Based on the national organization code, polluting enterprises can be extracted. The number of polluting enterprises, x12, was selected as the predictor variable for this class.
- (6)
- Points of interest (POIs). As the Open Geospatial Consortium (OGC) defined [49], a “point of interest” (POI) is a location for which information is available. A POI can be as simple as a set of coordinates, a name, and a unique identifier, or more complex. In practice, POIs are usually those places that serve a public function. As such, POIs generally exclude facilities such as private residences, but include many private facilities that seek to attract the general public such as retail businesses, amusement parks, industrial buildings, etc. POI data were primarily obtained from the Open Street Map [49] for the area of Beijing. The number of POIs x13 was selected as the predictor variable for this class.
- (7)
- Distance to the city center. Because most companies and people are distributed in the heart of the city, the distance to the city center x14 from the different monitoring points was selected as a predictor variable.
- (8)
- Buildings. Buildings influence the distribution of people and environmental pollutants. Building data were obtained from Open Street Map [47], and a geometric correction was needed for the source data. The area at the top of building, x15, was used as a predictor variable.
- (9)
- Natural landscape. The natural landscape is different from the land uses discussed above and is defined as the area x16 that has not been affected by human activities. Therefore, area was selected as the predictor variable for this class. Natural landscape information was also collected from Open Street Map [47].
2.3. Self-Adaptive Revised LUR Model
2.3.1. Traditional LUR Model
2.3.2. Self-Adaptive Revised LUR Model
3. Results from the Constructed Self-Adaptive Revised LUR Model for Beijing
3.1. Data Processing and Predictor Variable Screening
3.2. Typical LUR Model Construction and Evaluations of Initial PM2.5 Concentrations
- (1)
- The typical LUR model (which is also called the general LUR model in this paper) was constructed as follows. As shown in Figure 3, after the optimal semi-diameter buffer for each variable has been selected via correlation analysis, the correlation analyses between each two predictor variables should be used to remove the collinear variable based on the rules described in Section 2.2.1. The general LUR model for winter 2014 (the predicted R-squared value is 0.736, the adjusted R-squared value is 0.679 and the p-value of the model is 5.405 × 10−7) is expressed as follows:
- (2)
- Initial PM2.5 concentration evaluation
3.3. Self-Adaptive LUR Model Computation
- k represents the sampled points, with a total of 1119 sampled points;
- t represents the monitoring site, with a total of 35 monitoring sites;
- i represents an individual variable (16 variables were considered);
- j represents the buffer, with a total of 10 buffers;
- Sk,i,j represents the value of variable i in buffer j for the sampled point k;
- Xt,i,j represents the value of variable i in buffer j for the monitored site t;
- Xk,i,j represents the value of variable i in buffer j for the out-of-range sampling point k;
- Step 1: define k as the current computation point id, i as the current computation variable id, and j as the current computation buffer id;
- Step 2: define the loop for k with an initial value of 0; if k < 1119, k increases by one;
- Step 3: define the loop for i with an initial value of 0; if i < 16, i increases by one;
- Step 4: define the loop for j with an initial value of 0; if j < 10, j increases by one;
- Step 5: if Sk,i,j equals 0, go step 4; otherwise, go step 6;
- Step 6: find the maximum value of Coi,j as the best related buffer j (i.e., Coi,best); if found, go to step 3 until i = 16; then go to step 7;
- Step 7: compute an array x to store all values of the variables at the 35 monitoring sites using the selected best buffer, i.e., x[i][t] = xt,i,best;
- Step 8: compute the regression coefficients of A,B,C, … T; [Ak,Bk,Ck … Tk] = regress (X[1],X[2], … X[16]), if k = 1119, end; otherwise, go to step 1.
3.4. PM2.5 Map Generation
4. Discussion of the Results
4.1. Accuracy Analysis
4.2. Comparison with the Typical LUR Method
- (1)
- SPER
- (2)
- Accuracy
4.3. Spatial Variations of the PM2.5 Concentrations
4.4. Reliability, Superiority, and Limitations of the Revised LUR Model and Future Research
- (1)
- Reliability of the self-adaptive revised LUR model: The accuracy analysis showed that the overall accuracy is satisfactory, and the analysis of spatial variations in accuracy showed that the accuracy was highest near the city center, which is consistent with the fact that the monitoring sites are concentrated in central Beijing. The spatial variation analysis showed that the PM2.5 concentrations were low to the north, particularly in the northwest region of Beijing, and high in the southern and central regions of Beijing. The spatial variation analysis results were consistent with the fact that the northern region of Beijing is mountainous and contains few people; thus, less transportation is used in this region than in other regions, resulting in reduced pollutant concentrations. In the central region, a high population density and heavy traffic resulted in high pollution levels. Furthermore, the area south and southwest of Beijing, which is adjacent to Hebei province, contains many polluting enterprises. Consequently, high PM2.5 concentrations were found in this region. Therefore, the results are consistent and show the reliability of the self-adaptive revised LUR model. An error distribution map is shown in Figure 8, which is consistent with the results that suburban environmental evaluation sites have low accuracies in Section 4.1. The spatial autocorrelated analysis also showed the errors are not autocorrelated, which increased the reliability of this model.
- (2)
- Superiority of the self-adaptive LUR model: The SPER of the self-adaptive LUR model relative to the typical LUR model increased from 75% to 90%. In addition, the accuracy increased with RMSE from 20.643 μg/m3 to 17.443 μg/m3. Meanwhile, the adjusted R-squared value for the general LUR model was 0.679, and the adjusted R-squared values for the self-adaptive LUR models for the out-of-range sampling points were more than 0.5. Hence, the self-adaptive revised LUR model was superior to the typical LUR model.
- (3)
- Limitations of the self-adaptive LUR model: When the typical LUR model does not result in an SPER of at least 80%, or when negative values are observed in the prediction results, the self-adaptive revised LUR model can be used for improving the SPER and accuracy. If the SPER is at least 90%, the self-adaptive LUR model will not improve the accuracy of the typical LUR model. Thus, if the explanation ability of the model is high and we want to improve the accuracy, the best predictor variables should be chosen because previous studies have indicated that more comprehensive predictor variables result in more accurate final LUR models.
- (4)
- Future work: The effectiveness of the self-adaptive revised LUR model for estimating the PM2.5 concentrations for winter 2014 in Beijing is shown in this study. The presented approach will be effective for estimating the concentrations of other pollutants and other periods (e.g., spring, summer, and autumn of a year) in the future. Furthermore, this method can be used at high spatial scales, such as the national scale, because more sampled points will be outside of the data range, and the self-adaptive revised LUR model will be more effective. However, we used several open-sourced predictor variables. Thus, if possible, more predictor variables (i.e., meteorological data) should be used in future studies.
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Predicted Variable Type | Predicted Variable Name | Unit |
---|---|---|
Land use | Cropland | Area (unit: m2) |
Forest land | Area (unit: m2) | |
Garden plots | Area (unit: m2) | |
Urban land | Area (unit: m2) | |
Water | Area (unit: m2) | |
Terrain | Mean elevation | Height (unit: m) |
Mean slope degree | Angle (unit: degree) | |
Transport | Street | Length (unit: m) |
Railway and subway | Length (unit: m) | |
Waterway | Length (unit: m) | |
Population | Population | number |
Polluting enterprise | Polluting enterprise | number |
Point of interest | Point of interest | number |
Distance to city center | Distance to city center | Length (unit: m) |
Buildings | Buildings | Area (unit: m2) |
Natural landscape | Natural landscape | Area (unit: m2) |
Variable Type | Buffer Semi-Diameter | 500 m (j = 1) | 1000 m (j = 2) | 1500 m (j = 3) | 2000 m (j = 4) | 2500 m (j = 5) | 3000 m (j = 6) | 3500 m (j = 7) | 4000 m (j = 8) | 4500 m (j = 9) | 5000 m (j = 10) |
---|---|---|---|---|---|---|---|---|---|---|---|
Land use | Cropland (i = 1) | 0.632 | 0.641 | 0.592 | 0.531 | 0.486 | 0.446 | 0.418 | 0.389 | 0.373 | 0.348 |
Forest land (i = 2) | -- | −0.355 | −0.385 | −0.387 | −0.393 | −0.413 | −0.434 | −0.452 | −0.467 | −0.479 | |
Garden plots (i = 3) | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | |
Urban land (i = 4) | -- | -- | 0.424 | -- | -- | -- | -- | -- | -- | -- | |
Water (i = 5) | -- | -- | −0.324 | −0.332 | −0.334 | −0.334 | −0.329 | −0.324 | -- | -- | |
Terrain | Mean Elevation (i = 6) | −0.326 | −0.341 | −0.348 | −0.352 | −0.358 | −0.364 | −0.368 | −0.373 | −0.379 | −0.383 |
Mean slope (i = 7) | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | |
Transport | Street (i = 8) | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
Railway and subway (i = 9) | -- | -- | -- | 0.428 | -- | -- | -- | -- | -- | -- | |
Waterway (i = 10) | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | |
Population (i = 11) | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | |
Polluting enterprise (i = 12) | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | |
Point of interest (i = 13) | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | |
Distance to city center (i = 14) | −0.633 | ||||||||||
Buildings (i = 15) | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | |
Natural landscape (i = 16) | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
Predictor Variable | Predictor Variable Name (Unit) | Best Buffer Semi-Diameter | Correlation Coefficient | p-Value |
---|---|---|---|---|
Land use | Urban land x1 (m2) | -- | -- | -- |
Cropland x2 (m2) | 1000 | 0.641 | 0.000 | |
Forest x3 (m2) | 5000 | −0.479 | 0.004 | |
Garden plots x4 (m2) | -- | -- | -- | |
Water x5 (m2) | 3000 | −0.334 | 0.049 | |
Terrain | Elevation x6 (m) | 5000 | −0.383 | 0.023 |
Slope x7 (degree) | -- | -- | -- | |
Transport | Street x8 (m) | -- | -- | -- |
Railway and subway x9 (m) | 2000 | 0.428 | 0.010 | |
Waterway x10 (m) | -- | -- | -- | |
Population | Population x11 (people) | -- | -- | -- |
Polluted enterprises | Polluted enterprises x12 (number) | -- | -- | -- |
POI | POI x13 (number) | -- | -- | -- |
Distance to city center | Distance to city center x14 (m) | -- | −0.633 | 0.00 |
Buildings | Buildings x15 (m2) | -- | -- | -- |
Natural landscape | Natural landscape x16 (m2) | -- | -- | -- |
Error | ME (μg/m3) | RMSE (μg/m3) | SD (μg/m3) | MER (%) |
---|---|---|---|---|
Self-adaptive LUR models (Final map accuracy) | 1.296 | 17.443 | 17.395 | 14.658 |
Typical model (Final map accuracy) | 9.982 | 20.643 | 18.069 | 19.488 |
Typical model (LOO cross validation) | 1.438 | 24.7389 | 24.6971 | 21.365 |
Monitoring Sites in the Different Regions | ME (μg/m3) | RMSE (μg/m3) | SD (μg/m3) | MER (%) | |
---|---|---|---|---|---|
Urban environmental evaluation site | Self-adaptive LUR models | 0.768 | 11.897 | 11.873 | 10.598 |
Typical model | 13.414 | 17.546 | 11.311 | 17.147 | |
Suburban environmental evaluation site | Self-adaptive LUR models | 5.070 | 18.748 | 18.050 | 17.475 |
Typical model | 12.600 | 19.792 | 15.263 | 20.369 | |
Regional background control site | Self-adaptive LUR models | 1.243 | 25.946 | 25.916 | 23.004 |
Typical model | −2.112 | 28.639 | 28.561 | 26.371 | |
Traffic pollution monitoring site | Self-adaptive LUR models | −5.666 | 8.629 | 6.508 | 6.526 |
Typical model | 12.914 | 15.298 | 8.201 | 13.535 |
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Hu, L.; Liu, J.; He, Z. Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China. Sustainability 2016, 8, 786. https://doi.org/10.3390/su8080786
Hu L, Liu J, He Z. Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China. Sustainability. 2016; 8(8):786. https://doi.org/10.3390/su8080786
Chicago/Turabian StyleHu, Lujin, Jiping Liu, and Zongyi He. 2016. "Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China" Sustainability 8, no. 8: 786. https://doi.org/10.3390/su8080786