Assessing the Effects of Urban Morphology Parameters on PM2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method
Abstract
:1. Introduction
2. Methodology
2.1. Study Area
2.2. Measurement of PM2.5 and Microclimate Parameter
- (1)
- Observe the temporal and spatial variation of PM2.5 concentration. The hourly PM2.5 concentration data of each measuring point for 21 days were collected, and then the hourly average was calculated to observe the temporal distribution characteristics of PM2.5 concentration. The PM2.5 concentration data of each measuring point at 10:00 and 22:00 for 21 days were collated, and then the mean value of these two times was calculated to observe the spatial distribution characteristics of PM2.5 concentration.
- (2)
- Observe the influence of urban microclimate on PM2.5 concentration. According to the temporal distribution characteristics of PM2.5 concentration, the typical moments when PM2.5 concentration changes were selected. The PM2.5 concentration and microclimate data at the corresponding moments of each measuring point for 21 days were collected, and then the average value at the corresponding moments was calculated to observe the influence of microclimate change on PM2.5 concentration.
- (3)
- Collect data for predictive model training and validation. The hourly PM2.5 concentration and microclimate data of each measuring point for 21 days were collected and combined with the subsequent urban morphology and other related data, finally, 12,600 sets of data were obtained, and then the training and verification of the prediction model was carried out.
2.3. Urban Morphology Parameters Analysis
2.3.1. Urban Morphology Parameters Selection and Computation
- (1)
- The parameters should significantly affect PM2.5 concentration.
- (2)
- The parameters should be easy to extract and calculate.
- (3)
- The parameters affect the design.
- (4)
- Parameter redundancy should be avoided.
2.3.2. Determination of Influence Radius of Urban Morphology Parameters
2.4. Gradient Boosted Regression (GBRT) Trees Model
2.4.1. Model Construction Principle
- (a)
- For i = 1, 2, …, N. The negative gradient direction of the loss function was calculated, and the predicted value of the model was obtained, which was used as the prediction residual. The negative gradient of the i-th training data is as follows:
- (b)
- Build a regression tree on the basis of rmi, and obtain the leaf node area Rmj of the m-th tree. Predict the leaf node area of the decision tree to obtain an approximate value of the fitting residual.
- (c)
- For j = 1, 2, …, J. Linear search is used to obtain the value in the range of leaf nodes. Minimize the loss function. The best residual fitting value of each blade is as follows:
- (d)
- Update the regression tree:
2.4.2. Model Construction and Comparative Validation
3. Results and Analysis
3.1. Temporal and Spatial Distribution of PM2.5 at Urban Block Scale
3.2. Correlation Analysis of PM2.5 Concentration and Microclimate
3.3. Model Analysis and Comparison of Validation Results
3.4. The Influence of Urban Spatial Morphology on PM2.5 Distribution
4. Discussion and Urban Design Recommendations
- (1)
- Horizontal layout of buildings: Building density is the urban morphology factor that has the greatest impact on PM2.5 concentration, with an impact degree of 57%; plot ratio and building volume density have an impact degree of 33% and 22% respectively. Therefore, building density parameters should be given priority.
- (2)
- Vertical layout of buildings: the influence degree of average building height and standard deviation of building height is 49% and 12% respectively, so it is necessary to make reasonable restrictions on building height. Attention should also be paid to the diversity of building height.
- (3)
- Existing buildings: it is unrealistic to demolish buildings on a large scale, but the existing urban spatial form can be improved. The impact degree of frontal area index and road density is 11% and 23% respectively. The essence of the impact of road density on PM2.5 concentration comes from automobile exhaust emissions. Based on this, removing part of the windward wall and controlling street vehicles is a practical solution.
5. Conclusions
- (1)
- There are significant temporal and spatial differences in PM2.5 concentration. The temporal difference indicates that the daily variations in PM2.5 concentration are influenced by human activities and meteorological factors. The curves of the average daily variations of PM2.5 concentration are similar, with two peaks. The spatial difference indicates that the variation in PM2.5 concentration is influenced by urban morphology factors, and PM2.5 concentration is different under different urban morphology.
- (2)
- There is a significant linear relationship between microclimate and PM2.5 concentration. Wind speed and temperature are negatively correlated with PM2.5 concentration, while humidity is positively correlated with PM2.5 concentration. However, both microclimate and PM2.5 concentrations are affected by urban morphology, indicating that urban morphology, microclimate, and PM2.5 concentration interact with each other.
- (3)
- Compared with other models, it is found that the gradient boosted regression trees (GBRT) prediction model has higher prediction accuracy and stability. The GBRT model was used to rank the influencing factors, and it was found that, except for the local PM2.5 concentration and climate data released by meteorological stations, urban morphology factors contributed significantly to the change of PM2.5 concentration. The highest influence degree is building density and average building height, followed by plot ratio, road density, building volume density, and finally standard deviation of building height and frontal area index.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
RD | Road density (%) |
FAI | Frontal area index (%) |
BVD | Building volume density (%) |
BD | Building density (%) |
PR | Plot ratio |
AH | Average building height (m) |
SDBH | Standard deviation of building height (m) |
T | Measured hourly temperature (°C) |
WIND | Measured hourly wind speed (m/s) |
RH | Measured hourly humidity (%) |
WeaT | Hourly temperature released by the Meteorological Observatory (°C) |
WeaWIND | Hourly wind speed released by the Meteorological Observatory (m/s) |
WeaRH | Hourly humidity released by the Meteorological Observatory (%) |
WeaPM2.5 | Hourly PM2.5 concentration released by the Meteorological Observatory (μg/m3) |
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Name | Usage | Technical Parameter |
---|---|---|
NK5500 weather station | Wind speed, Temperature, Humidity | Wind speed measurement range is 0.6–60 m/s, accuracy is ±3%, 1 inch|25 mm diameter impeller with precision axle and low-friction Zytel® bearings; Temperature measurement range is −29–70 °C, accuracy ±0.5 °C, platinum resistance temperature sensor; Humidity measurement range is 0–100%, accuracy is ±2%, polymeric capacitance humidity sensor. The measurement range is the number of particles in the air per 0.01 cubic feet of volume. The unit is μg/m3. Laser scattering method. |
DylosDC1700 particle detector | PM2.5 concentration | Two kinds of particles of 0.5 μm and 2.5 μm can be detected. This value divided by 100 is the mass concentration of PM2.5, commonly used in China. |
8:00–10:00 | 12:00–15:00 | 19:00–22:00 | |
---|---|---|---|
WeaT (°C)/WeaRH (%)/WeaWIN (m/s)/WeaPM2.5 (μg/m3) | |||
1 December 2020 | −12.6/70/2.8/112.7 | −9.2/54.2/3/86.8 | −12.4/72/2.6/117 |
2 December 2020 | −13.1/69.3/3.3/79 | −10.2/61.8/2.9/70.3 | −13.1/74/2.5/98.5 |
9 December 2020 | −8.6/53.3/5.2/66 | −5.4/60.5/5.3/90 | −7.9/67.3/2.6/115 |
16 December 2020 | −20.6/63.7/3/78 | −16.4/51.5/3.9/65.5 | −17.9/52.8/4.5/77.8 |
22 December 2020 | −7/72.7/6.2/115 | −5.7/62.5/4.7/134.3 | −13.1/88.5/0.7/147.3 |
24 December 2020 | −17/71.7/2.5/114.7 | −14.1/59.5/2.6/139 | −19.4/86/0.73/149.3 |
1 January 2021 | −23.2/72/2.4/62.3 | −18.6/57.5/2.5/95 | −22.8/65/1.6/83.5 |
4 January 2021 | −20.9/65.7/3.3/77.3 | −17.4/55.3/2.9/92.3 | −20.9/73.3/2.2/65.5 |
5 January 2021 | −21.5/65.7/2.7/56.3 | −17.5/55.3/3/77.5 | −19.8/64.8/2.4/99 |
9 January 2021 | −22.7/68/2.4/99 | −17.9/54/3.2/155.8 | −19.8/65.5/2/135.5 |
11 January 2021 | −15.2/67.3/3.7/101.3 | −12.1/57/3.4/90.3 | −15.1/78.8/1.3/74.5 |
12 January 2021 | −15.7/84.3/1.6/102.3 | −8.9/73.8/2.7/96.5 | −8.6/92.5/2/91.3 |
13 January 2021 | −17/79.3/4/83.7 | −14.3/70.8/3.8/104.5 | −16.4/84/1.9/96.8 |
14 January 2021 | −19.3/75.7/2.3/112.3 | −16.4/66.3/1.9/99.3 | −18.7/77.5/1.4/53.8 |
20 January 2021 | −13.9/83/1.7/68 | −4.2/85.5/4.8/99 | −6.4/83.3/2.9/68.8 |
21 January 2021 | −15.3/82.7/1.2/107.7 | −8.9/56.8/3/198.8 | −15.9/71.3/2.4/70 |
23 January 2021 | −16/77/1/142.3 | −7.6/52.8/1.3/162.3 | −13.8/80.5/1.3/214.5 |
24 January 2021 | −11.2/83.3/0.7/263.3 | −2.5/56.3/1.3/210.5 | −13.8/88.3/1.1/62 |
8 February 2021 | −10.5/78/2.1/118 | −3.2/63/2.3/116 | −14.5/82/2.4/121 |
14 February 2021 | −8.4/62/3.2/89 | −2.3/54/3.6/78 | −11.6/76/3.5/95 |
15 February 2021 | −7.6/79/2.8/91 | −1.9/69/3.4/88 | −10.8/86/2.6/111 |
Urban Morphology Factor | Unit | Equation of Calculation | Theoretical Meaning |
---|---|---|---|
RD | % | Traffic pollution intensity | |
FAI | % | The blocking effect of the buildings in the plot on the airflow | |
BVD | % | The spatial density of the buildings in the plot | |
BD | % | The level of building density in the horizontal direction within the plot | |
PR | - | The overall volume and development intensity of the buildings in the plot | |
AH | m | Vertical building development intensity | |
SDBH | - | The degree of difference and dislocation of the vertical building height within the plot |
Urban Morphology Factor | RD | FAI | BVD | BD | PR | AH | SDBH |
---|---|---|---|---|---|---|---|
No.1: R2/sig (50 m) | 0.696/0.0 | 0.633/0.5 | 0.766/0.0 | 0.580/0.0 | 0.635/0.0 | 0.663/0.09 | 0.731/0.06 |
No.2: R2/sig (100 m) | 0.754/0.0 | 0.685/0.0 | 0.829/0.0 | 0.628/0.0 | 0.605/0.0 | 0.718/0.0 | 0.792/0.0 |
No.3: R2/sig (200 m) | 0.792/0.0 | 0.720/0.01 | 0.87/0.0 | 0.660/0.02 | 0.794/0.0 | 0.754/0.0 | 0.890/0.0 |
No.4: R2/sig (300 m) | 0.895/0.0 | 0.814/0.0 | 0.915/0.03 | 0.846/0.0 | 0.750/0.0 | 0.752/0.02 | 0.840/0.0 |
No.5: R2/sig (400 m) | 0.625/0.0 | 0.568/0.0 | 0.688/0.0 | 0.521/0.0 | 0.753/0.0 | 0.795/0.0 | 0.656/0.0 |
No.6: R2/sig (500 m) | 0.533/0.1 | 0.485/0.0 | 0.586/0.0 | 0.444/0.0 | 0.640/0.0 | 0.852/0.01 | 0.560/0.2 |
GBRT | MLR | RF | DT | |
---|---|---|---|---|
MAE (μg/m3) | 1.452 | 3.690 | 1.631 | 2.308 |
MSE (μg/m3) | 3.246 | 8.872 | 4.285 | 5.197 |
R2 | 0.978 | 0.791 | 0.966 | 0.894 |
Model | Advantage | Disadvantage | ||
---|---|---|---|---|
Empirical model | Linear regression model | Land use regression (LUR) | Fast calculation speed | Failed to capture the nonlinear relationships |
Multiple linear regression (MLR) | Fast calculation speed | Failed to capture the nonlinear relationships | ||
Machine learning method | Decision tree (DT) | Capture the nonlinear relationships | Low prediction accuracy | |
Random forest (RF) | Capture the nonlinear relationships; Rank the influencing variables based on their importance | - | ||
Gradient boosted regression trees (GBRT) | Capture the nonlinear relationships; Rank the influencing variables based on their importance | - | ||
Support vector machine (SVM) | Capture the nonlinear relationships | Cannot rank the influencing variables based on their importance | ||
Multi-layer perceptron | Capture the nonlinear relationships | Cannot rank the influencing variables based on their importance | ||
Sequence learning | Capture the nonlinear relationships | Cannot rank the influencing variables based on their importance | ||
Deterministic model | - | Weather research and forecasting (WRF) | Applicable to macroscale | Limited the analysis of air quality at microscales |
Community multiscale air quality (CMAQ) | Applicable to macroscal | Limited the analysis of air quality at microscales |
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Cui, P.; Dai, C.; Zhang, J.; Li, T. Assessing the Effects of Urban Morphology Parameters on PM2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method. Sustainability 2022, 14, 2618. https://doi.org/10.3390/su14052618
Cui P, Dai C, Zhang J, Li T. Assessing the Effects of Urban Morphology Parameters on PM2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method. Sustainability. 2022; 14(5):2618. https://doi.org/10.3390/su14052618
Chicago/Turabian StyleCui, Peng, Chunyu Dai, Jun Zhang, and Tingting Li. 2022. "Assessing the Effects of Urban Morphology Parameters on PM2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method" Sustainability 14, no. 5: 2618. https://doi.org/10.3390/su14052618