# Quantifying the Independent Influences of Land Cover and Humidity on Microscale Urban Air Temperature Variation in Hot Summer: Methods of Path Analysis and Genetic SVR

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Field Measurements

#### 2.2. Parameter Derivation and Variable Definition

_{s}is the saturation vapor pressure of water, which is a function of temperature only. Theoretically, the saturation vapor pressure over a plane surface of pure water is calculated by using Clapyron–Clausius equation. In practice, it is usually satisfactory to use the Tetens formulation [40], which is given as

#### 2.3. Path Analysis

#### 2.4. Genetic Support Vector Regression

#### 2.4.1. Support Vector Regression

_{1}, y

_{1}), …, (x

_{n}, y

_{n})}, the regression function that approximates the true relationship between y

_{i}and x

_{i}in SVR is specified in the following form:

_{i}and α

_{i}* are Lagrange multipliers, and ⟨φ(x

_{i}), φ(x

_{j})⟩ donates the inner product of φ(x

_{i}) and φ(x

_{j}). Since Equation (7) is a quadratic function depending only on α

_{i}and α

_{i}*, it can be solved by Sequential Minimal Optimization (SMO) algorithm or Quadratic Programming (QP) techniques. Furthermore, using the so-called kernel function expressed as an inner product, k(x

_{i}, x

_{j}) = ⟨φ(x

_{i}), φ(x

_{j})⟩, the regression function of Equation (4) can be given as

#### 2.4.2. Genetic Algorithm to Optimize SVR Parameters

_{i}is the predicted value of the regression model with respect to x

_{i}; y

_{i}is the ith observed value of y in the training sample. In addition, the coefficient of determination was calculated to indicate the prediction accuracy of the model in this study, which is given as

## 3. Results and Discussion

#### 3.1. Distributions of ∆Ta and ∆RH of the Two Land Covers

#### 3.2. Effects of Variables on ∆Ta in Priori Models

#### 3.3. Independent Contributions of ∆X and ∆RH

#### 3.4. Implication for High Air Temperature Mitigation

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 4.**Over flowchart of genetic support vector regression (SVR) training to optimize parameters.

**Figure 5.**Box plots of Air Temperature Difference (∆Ta) (

**a**) and Relative Humidity Difference (∆RH) (

**b**).

**Figure 7.**Air Temperature Difference (∆Ta) varying with Relative Humidity Difference (∆RH) at ∆X = 0.

**Figure 8.**Air Temperature Difference (∆Ta) varying with Relative Humidity Difference (∆RH) at ∆X = 30 m.

**Table 1.**Kruskal–Wallis one-way analysis of variance (ANOVA) of Air Temperature Difference (∆Ta) and Relative Humidity Difference (∆RH) between concrete land and bush woodland.

Variable | Chi-Square | DF | Prob (> Chi-Square) |
---|---|---|---|

Air Temperature Difference (∆Ta) | 94.13 | 1 | 0.0000 |

Relative Humidity Difference (∆RH) | 83.26 | 1 | 0.0000 |

Response Variable | Path | Standardized Path Coefficient | t-Statistic | Prob (> |t|) | R-Square |
---|---|---|---|---|---|

Air Temperature Difference (∆Ta) | ∆Ta ← ∆X ^{1} | 0.143 | 4.181 | 0.000 | 0.870 |

∆Ta ← ∆RH | −0.822 | −21.415 | 0.000 | ||

Relative Humidity Difference (∆RH) | ∆RH ← ∆X | −0.740 | −12.741 | 0.000 | 0.547 |

^{1}Representing the path from ∆X to ∆Ta.

Response Variable | Path | Standardized Path Coefficient | t-Statistic | Prob (> |t|) | R-Square |
---|---|---|---|---|---|

Air Temperature Difference (∆Ta) | ∆Ta ← ∆X | 0.709 | 13.025 | 0.000 | 0.574 |

∆Ta ← ∆SPH | −0.109 | −1.764 | 0.040 | ||

Specific Humidity Difference (∆SPH) | ∆SPH ← ∆X | −0.387 | −4.776 | 0.000 | 0.150 |

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**MDPI and ACS Style**

Shi, W.; Wang, N.; Xin, A.; Liu, L.; Hou, J.; Zhang, Y.
Quantifying the Independent Influences of Land Cover and Humidity on Microscale Urban Air Temperature Variation in Hot Summer: Methods of Path Analysis and Genetic SVR. *Atmosphere* **2020**, *11*, 1377.
https://doi.org/10.3390/atmos11121377

**AMA Style**

Shi W, Wang N, Xin A, Liu L, Hou J, Zhang Y.
Quantifying the Independent Influences of Land Cover and Humidity on Microscale Urban Air Temperature Variation in Hot Summer: Methods of Path Analysis and Genetic SVR. *Atmosphere*. 2020; 11(12):1377.
https://doi.org/10.3390/atmos11121377

**Chicago/Turabian Style**

Shi, Weifang, Nan Wang, Aixuan Xin, Linglan Liu, Jiaqi Hou, and Yirui Zhang.
2020. "Quantifying the Independent Influences of Land Cover and Humidity on Microscale Urban Air Temperature Variation in Hot Summer: Methods of Path Analysis and Genetic SVR" *Atmosphere* 11, no. 12: 1377.
https://doi.org/10.3390/atmos11121377