# Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials

#### 2.1. Study Area

#### 2.2. Data

_{2}, NO

_{2}, PM10, PM2.5, O

_{3}, CO). IAQI is calculated as follows:

_{2}, O

_{3}and NO

_{2}. (Although PM2.5 and PM10 update every 24 h). It can provide a great quantity of samples for all kinds of studies in the field of atmospheric pollutants. The monthly meteorological data come from the China Meteorological Data website. The monitoring network is composed of 23 ground-based monitoring sites scattered around the entire study area. These meteorological data are abstracted from the daily dataset of China Ground International Exchange Stations. The dataset is from 194 basic ground meteorological stations. Data items include: average air pressure, average air temperature, average water vapor pressure, mean relative humidity, average wind speed, evaporation, sunshine duration and precipitation. The time range of these data is from August 2014–May 2016. The coordinate system used for the location information of these stations is WGS84.

## 3. Methods

#### 3.1. Spatial Interpolation

#### 3.2. Spatial Autocorrelation

#### 3.3. Cross-Validation

#### 3.4. Interpolation Accuracy Evaluation

#### 3.5. Temporal Correlation

_{1}, x

_{2}, …, x

_{n}} and y = {y

_{1}, y

_{2}, …, y

_{n}} represent two different time series, r is in the range of [–1, 1]. R > 0 for positive correlation; r < 0 for negative correlation; r = 0 represents the absence of correlation. The greater the correlation, the higher the absolute value of r. It is generally believed that the absolute value of r is a micro correlation between 0 and 0.3, a real correlation between 0.3 and 0.5, a significant correlation between 0.5 and 0.8 and a high correlation between 0.8 and 1 [35].

## 4. Analysis of Temporal and Spatial Characteristics

#### 4.1. Temporal Characteristics

#### 4.2. AQI Mapping with IDW

#### 4.3. AQI Mapping with Kriging

#### 4.4. AQI Mapping with BME

#### 4.5. Cross-Validation and Comparison

## 5. Relationship between AQI and Meteorological Conditions

## 6. Conclusions

- (1)
- In recent years, AQI shows a clear periodicity, although overall, it has a downward trend. AQI fluctuates more drastically over time; the peak of AQI appeared in November, December and January.
- (2)
- Bayesian maximum entropy interpolation has a higher accuracy than kriging. IDW has the maximum error.
- (3)
- In the same year, the AQI of winter (November) and spring (February) is much worse than summer (May) and autumn (August). Additionally, the air quality has improved every quarter for three years. It proves that the government’s air quality management strategy has been effective in recent years.
- (4)
- The distribution of AQI has obvious spatial characteristics. For the study area, the most polluted areas of air quality are concentrated in Beijing, the southern part of Tianjin, the central-southern part of Hebei, the central-northern part of Henan and the western part of Shandong.
- (5)
- The average wind speed and average relative humidity have a real correlation. The calculated correlation coefficients using daily data provide support for association analysis on a finer scale. The effect of meteorological factors, such as wind, precipitation and humidity, on AQI is putative to have a temporal lag to different extents.
- (6)
- The AQI of a city with poor air quality will fluctuate greater than others when weather changes and has higher correlation with meteorological factors.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**The study area, its location in China and the monitoring stations of the air quality index (AQI) to be used.

**Figure 3.**AQI trend curves from August 2014–August 2016. (

**a**) Beijing, Tianjin, Hebei (

**b**) Henan; (

**c**) Shanxi; and (

**d**) Shandong. Date format: yyyy/mm.

**Figure 5.**AQI distribution map using inverse distance weighted (IDW) (the figure panel is read from left to right, from top to bottom: August 2014, November 2014, February 2015, May 2015, August 2015, November 2015, February 2016, May 2016, August 2016).

**Figure 6.**The distribution investigation of AQI data with several ESDA (exploratory spatial data analysis) techniques, such as histogram, quantile-quantile (Q-Q) diagram and 3D scatterplot. (

**a**) Distribution histogram; (

**b**) normal Q-Q plot; and (

**c**) trend analysis.

**Figure 7.**The empirical semivariogram models for nine selected months (the figure panel is read from left to right, from top to bottom: August 2014, November 2014, February 2015, May 2015, August 2015, November 2015, February 2016, May 2016, August 2016).

**Figure 8.**AQI distribution map using ordinary kriging (the figure panel is read from left to right, from top to bottom: August 2014, November 2014, February 2015, May 2015, August 2015, November 2015, February 2016, May 2016, August 2016).

**Figure 10.**AQI distribution map using Bayesian maximum entropy (the figure panel is read from left to right, from top to bottom: August 2014, November 2014, February 2015, May 2015, August 2015, November 2015, February 2016, May 2016, August 2016).

**Figure 11.**The accuracy of each interpolation method. (

**a**) August 2014; (

**b**) November 2014; (

**c**) February 2015; (

**d**) May 2015; (

**e**) August 2015; (

**f**) November 2015; (

**g**) February 2016; (

**h**) May 2016; and (

**i**) August 2016.

**Figure 13.**Temporal correlation analysis in Beijing, Tianjin and Zhengzhou. C

_{i}= [Beijing, Tianjin, Zhengzhou], F

_{i}= [precipitation anomaly percentage, precipitation, mean wind speed, average temperature, average water vapor pressure, average relative humidity].

**Figure 15.**Temporal correlation analysis in Beijing, Tianjin and Zhengzhou. C

_{i}= [Beijing, Tianjin, Zhengzhou], F

_{i}= [precipitation, mean wind speed, average temperature, average water vapor pressure, average relative humidity].

**Figure 16.**Temporal cross-correlation analysis between daily meteorological factors and AQI in Beijing considering temporal lag.

Station | Mean | Mean (2014) | Mean (2015) | Variance | Variance (2014) | Variance (2015) | Max | Min |
---|---|---|---|---|---|---|---|---|

Beijing | 117.68 | 119.20 | 116.17 | 5457.34 | 4640.34 | 6275.74 | 485 | 23 |

Tianjin | 104.38 | 108.90 | 99.89 | 3316.49 | 3228.31 | 3372.55 | 391 | 27 |

Baoding | 145.85 | 160.73 | 131.10 | 7485.18 | 8055.78 | 6501.22 | 500 | 35 |

Yangquan | 98.33 | 100.54 | 96.14 | 2083.73 | 1853.59 | 2308.03 | 360 | 26 |

Linfen | 87.65 | 84.36 | 90.90 | 1964.32 | 1747.82 | 2163.05 | 346 | 20 |

Zhengzhou | 129.78 | 134.62 | 124.98 | 4245.17 | 3755.58 | 4695.95 | 500 | 38 |

Month | Nugget | Parameter | Major Range | Partial Sill | Lag Size |
---|---|---|---|---|---|

August 2014 | 72.562 | 0.2 | 4.5 | 0 | 0.56249 |

November 2014 | 0 | 0.91016 | 1.6529 | 290.05 | 0.18965 |

February 2015 | 486.36 | 0.2 | 1.0211 | 0 | 0.12764 |

May 2015 | 0 | 0.88027 | 1.1841 | 120.27 | 0.13472 |

August 2015 | 0 | 0.43906 | 1.1841 | 108.53 | 0.13474 |

November 2015 | 165.83 | 0.2 | 3.2688 | 0 | 0.40860 |

February 2016 | 0 | 0.90488 | 1.1841 | 100.52 | 0.13717 |

May 2016 | 75.69 | 0.2 | 14.213 | 0 | 1.18445 |

August 2016 | 42.136 | 2 | 1.1841 | 6.6152 | 0.13353 |

**Table 3.**Cross-validation result of three interpolation methods. OK, ordinary kriging; BME, Bayesian maximum entropy.

Time | Method | MAE | RMSIE | Time | Method | MAE | RMSIE |
---|---|---|---|---|---|---|---|

August 2014 | IDW | 11.2923 | 14.8369 | November 2015 | IDW | 16.8145 | 21.3827 |

OK | 10.9151 | 14.1755 | OK | 13.3573 | 16.6038 | ||

BME | 9.4169 | 12.4072 | BME | 16.0600 | 20.0600 | ||

November 2014 | IDW | 17.8546 | 24.1299 | February 2016 | IDW | 9.6880 | 12.2206 |

OK | 14.0996 | 18.6090 | OK | 8.9560 | 11.3090 | ||

BME | 17.6734 | 21.8362 | BME | 9.2500 | 11.5400 | ||

February 2015 | IDW | 20.0113 | 26.6201 | May 2016 | IDW | 8.5534 | 10.8039 |

OK | 18.1584 | 24.6983 | OK | 8.9295 | 10.7937 | ||

BME | 17.7400 | 23.1100 | BME | 8.8525 | 11.0317 | ||

May 2015 | IDW | 9.2370 | 11.8869 | August 2016 | IDW | 7.8679 | 9.6368 |

OK | 9.2512 | 11.8708 | OK | 7.7776 | 9.8154 | ||

BME | 8.8569 | 11.6275 | BME | 7.5564 | 9.6982 | ||

August 2015 | IDW | 9.8277 | 12.1164 | ||||

OK | 9.9885 | 12.1584 | |||||

BME | 9.5299 | 11.7899 |

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## Share and Cite

**MDPI and ACS Style**

Yu, Z.; Zhong, S.; Wang, C.; Yang, Y.; Yao, G.; Huang, Q.
Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China. *ISPRS Int. J. Geo-Inf.* **2017**, *6*, 52.
https://doi.org/10.3390/ijgi6020052

**AMA Style**

Yu Z, Zhong S, Wang C, Yang Y, Yao G, Huang Q.
Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China. *ISPRS International Journal of Geo-Information*. 2017; 6(2):52.
https://doi.org/10.3390/ijgi6020052

**Chicago/Turabian Style**

Yu, Zhichen, Shaobo Zhong, Chaolin Wang, Yongsheng Yang, Guannan Yao, and Quanyi Huang.
2017. "Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China" *ISPRS International Journal of Geo-Information* 6, no. 2: 52.
https://doi.org/10.3390/ijgi6020052