# Evaluating the Effects of Urbanization Evolution on Air Temperature Trends Using Nightlight Satellite Data

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## Abstract

**:**

## 1. Introduction

## 2. Data

#### 2.1. Satellite Nightlights Time Series

#### 2.2. Air Temperature Datasets

## 3. Data preparation

- geo-localization of air temperature stations,
- pairing of temperature and nightlights data,
- gap-filling procedure for incomplete time series of temperature records.

#### 3.1. Geo-Localization of Air Temperature Stations

`°`) and, thus, provides a precise geo-localization of temperature stations. An iterative statistical-based procedure is performed in order to compare station coordinates in both Berkeley Earth and WMO datasets when available. When inconsistencies in the Berkeley Earth dataset are found, the related coordinates are corrected by using WMO data. Different scenarios could occur. These are described below and we refer to the flowchart in Figure 1 for further details.

- If the weather station from the Berkeley Earth dataset is not available in the WMO list and its spatial resolution is coarser than 30 arcsec, the station is removed.
- If the weather station from the Berkeley Earth dataset is not available in the WMO list and its spatial resolution is equal or more detailed than the 30 arcsec, the station is included in the final sample.
- If the weather station from the Berkeley Earth dataset is included in the WMO list and the spatial resolution provided by the Berkeley Earth dataset is coarser than 30 arcsec, station coordinates are corrected by using those provided by WMO and the station is included in the final sample.
- If the weather station from the Berkeley Earth dataset is included in the WMO list and the spatial resolution provided by the Berkeley Earth dataset is equal or more detailed than 30 arcsec, station coordinates from the two datasets are compared. In case of significant differences (i.e., the station is located in two different grid cells, see Figure 2 as an example), the original sources are consulted to precisely locate the station. Station metadata are, thus, corrected and the station is included in the final sample.

#### 3.2. Pairing of Temperature and Nightlights Data

_{is}) using the equation below.

_{ik}is the value in the kth pixel for the year i and k

_{tot}is the total number of pixels in the buffer around station s. For example, k

_{tot}= 9 in a 3 × 3 km buffer around the station and up to k

_{tot}= 49 in a 7 × 7 km buffer. In this regard, ${L}_{is}^{(3)}$ indicates that the average is made in 3×3 km buffer, whereas ${L}_{is}^{(7)}$ refers to a 7 × 7 km buffer. Buffers smaller than 3 km (i.e., 1 × 1 km and 2 × 2 km) are not considered in order to avoid the spatial noise due to a possible inaccurate geo-localization of air temperature stations. Selected buffers allow one to attenuate this spatial noise (i.e., the local noisy effect is reduced by considering neighboring pixels). The choice of analyzing more than one buffer is due to effects of urban warming that could be detected several kilometers away from the instrument site, even if the major impact is evident in the first kilometer [70].

#### 3.3. Gap-Filling Procedure for Incomplete Time Series of Temperature Records

_{ij}, where i is the year (from 1992 to 2013) and j is the month (from 1 to 12). Yearly data are derived by averaging available monthly data. Since some stations show gaps in monthly data, we apply a statistical procedure to estimate the annual mean temperature in the presence of limited missing data and then fill the gaps (see Text S1 in the Supplementary Materials).

_{i}(s) the number of monthly data available in year i, i = 1992…2013 (0 ≤ Nm

_{i}(s) ≤ 12). We also call Ny

_{j}(s) the number of available records for month j, j = 1…12 (0 ≤ Ny

_{j}(s) ≤ 22). The distribution of Nm

_{i}and Ny

_{j}values is reported in Figure 3. By performing a sensitivity analysis (see a detailed description of the method and outcomes in S1 Text in the Supplementary Materials), the acceptable number of missing values to perform the gap-filling procedure is identified. The thresholds Nm* = 9 and Ny* = 18 are selected: if Nm

_{i}(s) < Nm* (for any i) or Ny

_{j}(s) < Ny* (for any j) the air temperature station s is discarded from our analysis. Otherwise, missing data are reconstructed as follows: suppose the temperature observation is missing in station s for month j* in year i*. The reconstructed value is the average of the temperature values available for the same month in other years using the equation below.

_{j*}(s) is the number of available records for month j* and T

_{kj*}(s) are available temperature data for month j* in year k, k = 1,…,Ny

_{j*}(s), respectively. Table S2 in the Supplementary Materials provides an example of application of the performed gap-filling procedure.

## 4. Methods

#### 4.1. Trend Analysis

_{T}identifies the slope of the temperature regression line, a

_{T}is the intercept, and t is time. The corresponding linear regression model to fit L values versus time is shown below.

_{L}and a

_{L}indicate the slope and intercept of the nightlights regression line, respectively.

_{T}and p

_{L}for temperature and nightlights, respectively—corresponding to the empirically determined slope values on a two-tailed Student’s t distribution with n-2 degrees of freedom, where n = 22 is the sample size, e.g., the length of the observation period in years. The null hypothesis of the test is that there is no trend and we adopt a significance level α = 0.1, i.e., a 0.05 significance level on each tail of the distribution. In the following, we allocate positive significant trends in the class c = 1 (p-value ≥ 0.95), negative significant trends in the class c = 4 (p-value ≤ 0.05), while positive and negative non-significant trends are placed in the classes c = 2 (0.5 < p-value < 0.95) and c = 3 (0.05 < p-value < 0.5), respectively.

_{T}and p

_{L}are in class 1 (p value ≥ 0.95), this means that significantly positive temperature and nightlight variations occur in the considered buffer.

#### 4.2. Statistical Indicators to Measure the Agreement between Temperature and Nightlights Trends

_{Tc}and w

_{Lc}, with c = 1,…,4 by using:

_{Tc}and n

_{Lc}indicate the number of p

_{T}and p

_{L}values in the cth class and n

_{TOT}the total sample size in the study area. For Asia, n

_{TOT}= 1153 (whereas, for the whole globe, n

_{TOT}= 5530).

_{T}and V

_{L}, and we assign values to each station and class of significance c: (i) 1 for c = 1, (ii) 0.5 for c = 2 (iii) −0.5 for c = 3, and (iv) −1 for c = 4. Specifically:

_{T}and V

_{L}define the final score assigned to the station. We then compute the expected values of the two variables V

_{T}and V

_{L}based on the probability of occurrence in class c and value assigned to the stations, which is shown in the equations below.

_{T}] = 0.42, E[V

_{L}] = 0.43, σ

^{2}[V

_{T}] = 0.42, σ

^{2}[V

_{L}] = 0.59.

_{T}and V

_{L}.

_{T}and p

_{L}, which means increasing temperature T (c = 1,2) and decreasing nightlights trends L (c = 3,4) or vice versa. An increasing index is instead associated with increasing agreement. In the case of Asia, CI = 0.22.

## 5. Results

_{T}for more than 70% of the selected stations is in class c equal to 1 and 2 (w

_{T1}, w

_{T2}), with the sole exception of South America (Figure 6f), where negative and positive temperature trends are almost balanced. The global distribution of L is clearly bimodal, with two peaks in class 1 and class 4 (the latter are mostly related to stations in North America).

## 6. Discussion and Conclusions

_{T}values are mainly included in the first two classes (w

_{T1}, w

_{T2}).

## Supplementary Materials

_{i}≥ 9 and Ny

_{j}≥ 18. The percentages refer to the total sample of active and selected stations respectively. Figure S1: Slope of T (b

_{T}) and L (b

_{L}) regression trend lines at a global and continental scale, 4 × 4 km buffer. Sectors 1 and 3 correspond to agreeing trends while sectors 2 and 4 refer to discordant trends. The number of stations included in each sector is in bold as well as the number of stations with L systematically equal to zero (b

_{L}= 0) on the horizontal axis. Figure S2: P values density plots at a global and continental scale, 4x4 km buffer. The color scale represents the data density. Figure S3: Slope of T (b

_{T}) and L (b

_{L}) regression trend lines on a global and continental scale, 5 × 5 km buffer. Sectors 1 and 3 correspond to agreeing trends while sectors 2 and 4 refer to discordant trends. The number of stations included in each sector is in bold as well as the number of stations with L systematically equal to zero (b

_{L}= 0) on the horizontal axis. Figure S4: P values density plots at global and continental scales, 5 × 5 km buffer. The color scale represents the data density. Figure S5: Slope of T (b

_{T}) and L (b

_{L}) regression trend lines at a global and continental scale. 6 × 6 km buffer. Sectors 1 and 3 correspond to agreeing trends, while sectors 2 and 4 refer to discordant trends. The number of stations included in each sector is in bold, as well as the number of stations with L systematically equal to zero (b

_{L}= 0) on the horizontal axis. Figure S6: P values density plots at a global and continental scale. 6 × 6 km buffer. The color scale represents the data density. Figure S7: Slope of T (b

_{T}) and L (b

_{L}) regression trend lines at a global and continental scale. 7 × 7 km buffer. Sectors 1 and 3 correspond to agreeing trends, while sectors 2 and 4 refer to discordant trends. The number of stations included in each sector is in bold, as well as the number of stations with L systematically equal to zero (b

_{L}= 0) on the horizontal axis., Figure S8: P values density plots at a global and continental scale, 7 × 7 km buffer. The color scale represents the data density.

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 1.**Flowchart showing the main steps of the procedure of geo-localization of air temperature stations from the Berkeley Earth dataset. In this figure, the Berkeley Earth dataset is abbreviated to Berkeley.

**Figure 2.**Application of the geo-localization procedure of air temperature stations in the Nile Delta region. Metadata provided by WMO and Berkeley Earth datasets are compared. In some cases, coordinates differ, see e.g., El Minya station (i.e., green square).

**Figure 3.**Geo-localization of air temperature stations from the Berkeley Earth dataset and gap-filling procedure for temperature records (dataset: average monthly temperature, Quality Controlled). (

**a**) Consistency analysis of monthly and yearly availability of air temperature stations in the period from 1992 to 2013. The figure shows the number of times when it is possible to derive the temperature T using the available data records i.e., how many stations have the entire data series and, if they are incomplete, how many months per year are available per each station. (

**b**) Yearly availability of air temperature stations with 12 months of records per year. (

**c**) Locations of active (i.e., having at least one year of data, empty dots) and selected (i.e., meeting geo-localization and gap-filling requirements) between 1992 and 2013 (filled dots). Stations are color-coded based on the six considered regions: Africa in green, Asia in red, Europe in yellow, North America in purple, South America in blue, and Oceania in light blue.

**Figure 4.**Linear regression trend lines of air temperature T (°C) and nightlights L (-) records. Example for the air temperature station of Torino Caselle, IT (Berkeley Earth ID: 155990). (

**a**) Temperature time series: green colored dots refer to annual temperature values in the presence of 12 months of available data per year. Empty dots refer to temperature values obtained from the gap-filling procedure. The linear regression line and equation (Equation (3)) are shown in red. The average annual temperature for year i is also reported. (

**b**) Nightlights time series: purple colored dots refer to annual Digital Number values, where a 3 × 3 km buffer is considered ${L}_{is}^{(3)}$. The linear regression line and equation (Equation (4)) are shown in red. The average annual Digital Number value for the year i is also reported.

**Figure 5.**Slope and P value scatterplots of T and L regression trend lines at global and continental scales, 3 × 3 km buffer. Panels (

**a**–

**i**): slope of T (b

_{T}) and L (b

_{L}) regression trend lines. The number of stations included in each sector is in bold, as well as the number of stations with L systematically equal to zero (b

_{L}= 0) on the horizontal axis. Panels (

**l**–

**t**): p values density plots where the color scale represents the data density. Figure 5l–t show the joint distribution of p

_{T}and p

_{L}values, which is represented as density plots. Similar conclusions shown above can be drawn at the continental scale, with the majority of the p values in the upper-right corner (very large p values) for Asia, Africa, and South America (weaker signal), which implies a systematic presence of concordant increasing trends of nightlights and temperature. Europe and Oceania have a bimodal distribution, with a second high-density peak in the lower-right corner (increasing temperature with decreasing nightlights). Lastly, North America presents a clear concentration of data points in the lower-right corner of the diagram, which indicates increasing temperature trends and decreasing nightlights trends.

**Figure 6.**Spatial distribution of statistic indicators of temperature and nightlights trend with a 3 × 3 km buffer approximately. Main panel: standardized z values (Equation (16)) at the continental scale (

**a**–

**i**) probability of occurrence w of classes of significance from 1 (++) to 4 (--), where CI: concordance index (Equation (13)). E[CI]: expected mean of CI (Equation (14)). σ[CI]: standard deviation of CI (Equation (15)). w

_{T}: yellow bars, w

_{L}: blue bars.

**Table 1.**Probability of occurrence in the considered four classes of significance c. The example refers to Asia, with n

_{TOT}= 1153. Number and percentage of stations in Asia with significance increasing (c = 1, ++), non-significance increasing (c = 2, +), non-significance decreasing (c = 3, -), and significance decreasing trends (c = 4, --) based on observed p

_{T}and p

_{L}values.

c | n_{T} | w_{T} | n_{L} | w_{L} |
---|---|---|---|---|

1 (++) | 471 | 40.8% | 646 | 56% |

2 (+) | 388 | 33.7% | 177 | 15.4% |

3 (-) | 233 | 20.2% | 170 | 14.7% |

4 (--) | 61 | 5.3% | 160 | 13.9% |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Paranunzio, R.; Ceola, S.; Laio, F.; Montanari, A. Evaluating the Effects of Urbanization Evolution on Air Temperature Trends Using Nightlight Satellite Data. *Atmosphere* **2019**, *10*, 117.
https://doi.org/10.3390/atmos10030117

**AMA Style**

Paranunzio R, Ceola S, Laio F, Montanari A. Evaluating the Effects of Urbanization Evolution on Air Temperature Trends Using Nightlight Satellite Data. *Atmosphere*. 2019; 10(3):117.
https://doi.org/10.3390/atmos10030117

**Chicago/Turabian Style**

Paranunzio, Roberta, Serena Ceola, Francesco Laio, and Alberto Montanari. 2019. "Evaluating the Effects of Urbanization Evolution on Air Temperature Trends Using Nightlight Satellite Data" *Atmosphere* 10, no. 3: 117.
https://doi.org/10.3390/atmos10030117