#
Impacts of Built-Up Area Geometry on PM_{10} Levels: A Case Study in Brno, Czech Republic

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## Abstract

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## 1. Introduction

## 2. Methodology

#### 2.1. Data

#### 2.2. Measured Variables

- For measuring concentrations of PM${}_{10}$: instrument manufactured by Environment SA, Model MP101M (MP101M is the automatic and real-time particulate monitor, compliant with ISO 10473:2000 and for PM${}_{10}$ US-EPA (EQPM-0404-151) and EN 12341 (I-CNR 087/2004, F-LCSQA). It allows the continuous and simultaneous measurement of fine dust, not influenced by the physico-chemical nature, color, or shape of particulates, in measurement ranges up to 10,000 $\mathsf{\mu}$g·m${}^{-3}$, with the lowest detectable limit of 0.5 $\mathsf{\mu}$g·m${}^{-3}$ (24 h average), with fiberglass tape (with 3 years of autonomy for continuous sampling with daily cycles) and with a measurement accuracy of ± 5%.).
- For measuring temperature and humidity: instrument made by Vaisala, Type HMP 155 with radiation shield DTR503 (HMP 155 (Vaisala) is the humidity and temperature probe compliant with the standards EN 61326-1 and EN 550022. Humidity measurement is based on the capacitive thin film HUMICAP${}^{\circledR}$ polymer sensor and temperature measurement on the resistive platinum sensors (Pt100). It allows the relative humidity (RH) measurement in the full range (0–100% RH) and with an accuracy in the range from −20 ${}^{\circ}$C to + 40 ${}^{\circ}$C ± (1.0 + 0.008 × reading)% RH. The accuracy temperature measurement is in the range from −80 ${}^{\circ}$C to +20 ${}^{\circ}$C ± (0.226 − 0.0028 × temperature) ${}^{\circ}$C and in the range from + 20 ${}^{\circ}$C to + 60 ${}^{\circ}$C ± (0.055 + 0.0057 × temperature) ${}^{\circ}$C).
- For measuring wind direction and speed: instrument manufactured by Gill Instruments Limited, type WindSonic (WindSonic is 2-axis ultrasonic wind sensor for true “fit and forget” wind sensing; it has no moving parts (alternative to conventional cup and vane or propeller wind sensors), compliant with the standard EN 61326:1998. It allows the wind speed measurement up to 60 m·s${}^{-1}$ with the accuracy ±2% (at 12 m·s${}^{-1}$) and wind direction measurement in the full circle with the accuracy $\pm {2}^{\circ}$ (at 12 m·s${}^{-1}$).

#### 2.3. Methods

## 3. Results

#### 3.1. Frequency of Limit Value Exceedances for a Moving Average

#### 3.2. Comparison of PM${}_{10}$ Measurements

#### 3.3. Comparison of NO${}_{x}$ Measurements

#### 3.4. Regression Models for PM${}_{10}$ Prediction

#### 3.5. Influence of Wind on PM${}_{10}$ Levels

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**A map with the stations (left) and a traffic intensity map in the vicinity of the stations (right).

**Figure 3.**Measured variables: PM${}_{10}$ ($\mathsf{\mu}$g·m${}^{-3}$), NO${}_{x}$ ($\mathsf{\mu}$g·m${}^{-3}$), temperature (${}^{\circ}$C), humidity (%), wind speed (m·s${}^{-1}$), and wind direction (degrees); Arboretum station, left; Černá Pole station, right.

**Figure 4.**PM${}_{10}$ with a 24 h moving average ($\mathsf{\mu}$g·m${}^{-3}$) and limit value of 50 $\mathsf{\mu}$g·m${}^{-3}$.

**Figure 6.**Fitted models, standardized residuals, and measured vs. fitted values, PM${}_{10}$ in $\mathsf{\mu}$g·m${}^{-3}$.

**Figure 8.**Boxplots of PM${}_{10}$ according to wind directions, PM${}_{10}$ in $\mathsf{\mu}$g·m${}^{-3}$. n—the number of observations.

Variable | Description |
---|---|

$\mathit{PM}{10}_{t}$ | PM${}_{10}$ ($\mathsf{\mu}$g·m${}^{-3}$) |

${\mathit{NOx}}_{t}$ | NO${}_{x}$ ($\mathsf{\mu}$g·m${}^{-3}$) |

${T}_{t}$ | temperature (${}^{\circ}$C) |

${H}_{t}$ | humidity (%) |

${V}_{t}$ | wind speed (m·s${}^{-1}$) |

${D}_{t}$ | wind direction (degrees) |

${\mathit{RH}}_{t}$ | rush hours (a dummy variable equal to 1 for 7–10 AM, 3–5 PM; |

at other times, it is equal to 0) |

**Table 2.**Descriptive statistics of variables (n—the number of observations; Mean—arithmetic mean; Median—median; St. dev.—standard deviation; Min—minimum value; Max—maximum value; ${Q}_{0.25}$—lower quartile; ${Q}_{0.75}$—upper quartile; Skewness—skewness, Kurtosis—kurtosis).

Arboretum | n | Mean | Median | St. dev. | Min | Max | ${\mathbf{Q}}_{\mathbf{0}.\mathbf{25}}$ | ${\mathbf{Q}}_{\mathbf{0}.\mathbf{75}}$ | Skewness | Kurtosis |

$\mathit{PM}{\mathit{10}}_{t}$ | 1830 | 26.9 | 24.8 | 16.6 | 0.9 | 109.5 | 14.7 | 37.0 | 0.89 | 1.00 |

$\mathit{NO}{\mathit{x}}_{t}$ | 1827 | 43.5 | 32.2 | 36.2 | 4.7 | 348.7 | 22.1 | 50.2 | 2.89 | 11.74 |

${T}_{t}$ | 1830 | 6.4 | 6.1 | 5.5 | −8.6 | 22.5 | 2.5 | 9.8 | 0.30 | −0.11 |

${H}_{t}$ | 1829 | 59.6 | 58.0 | 23.2 | 9.0 | 103.0 | 41.0 | 77.0 | 0.14 | −0.96 |

${V}_{t}$ | 1830 | 1.6 | 1.4 | 1.1 | 0.0 | 6.7 | 0.9 | 2.2 | 1.15 | 1.19 |

$\sqrt{\mathit{PM}{\mathit{10}}_{t}}$ | 1830 | 4.9 | 5.0 | 1.6 | 0.9 | 10.5 | 3.8 | 6.1 | 0.04 | −0.32 |

${V}_{t}sin{D}_{t}$ | 1830 | −0.38 | −0.27 | 1.37 | −6.69 | 3.05 | −0.98 | 0.55 | −0.80 | 0.97 |

${V}_{t}cos{D}_{t}$ | 1830 | 0.46 | 0.68 | 1.26 | −4.37 | 4.13 | −0.40 | 1.27 | −0.54 | 0.53 |

Černá Pole | n | Mean | Median | St. dev. | Min | Max | ${\mathbf{Q}}_{\mathbf{0}.\mathbf{25}}$ | ${\mathbf{Q}}_{\mathbf{0}.\mathbf{75}}$ | Skewness | Kurtosis |

$\mathit{PM}{\mathit{10}}_{t}$ | 1871 | 23.5 | 21.2 | 13.5 | 2.1 | 87.3 | 13.5 | 31.5 | 0.86 | 0.85 |

$\mathit{NO}{\mathit{x}}_{t}$ | 1869 | 36.0 | 22.1 | 38.5 | 2.7 | 361.8 | 12.9 | 43.3 | 2.97 | 12.43 |

${T}_{t}$ | 1872 | 6.8 | 6.4 | 5.6 | −7.8 | 23.3 | 2.8 | 10.3 | 0.29 | −0.18 |

${H}_{t}$ | 1871 | 61.4 | 59.0 | 22.0 | 13.0 | 102.0 | 43.5 | 80.0 | 0.09 | −1.03 |

${V}_{t}$ | 1872 | 0.6 | 0.4 | 0.6 | 0.0 | 3.8 | 0.2 | 0.8 | 1.86 | 4.53 |

$\sqrt{\mathit{PM}{\mathit{10}}_{t}}$ | 1871 | 4.6 | 4.6 | 1.4 | 1.4 | 9.3 | 3.7 | 5.6 | 0.12 | −0.36 |

${V}_{t}sin{D}_{t}$ | 1872 | 0.05 | −0.01 | 0.56 | −1.98 | 2.22 | −0.27 | 0.36 | 0.32 | 0.85 |

${V}_{t}cos{D}_{t}$ | 1872 | −0.25 | −0.09 | 0.56 | −3.44 | 1.02 | −0.35 | 0.06 | −2.11 | 5.83 |

Dependent Variable: | |||
---|---|---|---|

$\sqrt{\mathit{PM}{\mathbf{10}}_{\mathbf{t}}}$ | |||

Parameter | Variable | Arboretum | Černá Pole |

${\beta}_{1}$ | 0.593 *** | 0.241 *** | |

(0.060) | (0.035) | ||

${\beta}_{2}$ | $\sqrt{\mathit{PM}{10}_{t-1}}$ | 0.878 *** | 0.946 *** |

(0.011) | (0.007) | ||

${\beta}_{3}$ | ${T}_{t}-{T}_{t-1}$ | 0.118 *** | 0.117 *** |

(0.033) | (0.017) | ||

${\beta}_{4}$ | ${T}_{t-1}-{T}_{t-2}$ | −0.094 *** | −0.052 *** |

(0.024) | (0.012) | ||

${\beta}_{5}$ | ${H}_{t}-{H}_{t-1}$ | 0.006 | 0.011 *** |

(0.005) | (0.003) | ||

${\beta}_{6}$ | ${V}_{t-1}sin{D}_{t-1}$ | 0.071 *** | 0.085 *** |

(0.014) | (0.018) | ||

${\beta}_{7}$ | ${V}_{t-1}cos{D}_{t-1}$ | 0.002 | 0.049 *** |

(0.015) | (0.018) | ||

${\beta}_{8}$ | ${\mathit{RH}}_{\mathit{t}}$ | 0.117 *** | 0.054 ** |

(0.040) | (0.022) | ||

Number of observations n | 1824 | 1866 | |

R${}^{2}$ | 0.807 | 0.919 | |

Adjusted R${}^{2}$ | 0.806 | 0.918 | |

Residual Std. Error | 0.722 (df = 1816) | 0.402 (df = 1858) | |

F Statistic | 1,083.102 *** (df = 7; 1816) | 2,993.700 *** (df = 7; 1858) |

Parameter | Variable | F-Test | p-Value |
---|---|---|---|

${\beta}_{1}$ | 25.19828 | 5.41837$\times {10}^{-7}$ | |

${\beta}_{2}$ | $\sqrt{{\mathit{PM}}_{t-1}}$ | 25.51096 | 4.61270$\times {10}^{-7}$ |

${\beta}_{3}$ | ${T}_{t}-{T}_{t-1}$ | 0.00008 | 0.99266 |

${\beta}_{4}$ | ${T}_{t-1}-{T}_{t-2}$ | 2.65500 | 0.10331 |

${\beta}_{5}$ | ${H}_{t}-{H}_{t-1}$ | 0.80706 | 0.36905 |

${\beta}_{6}$ | ${V}_{t-1}sin{D}_{t-1}$ | 0.26454 | 0.60705 |

${\beta}_{7}$ | ${V}_{t-1}cos{D}_{t-1}$ | 2.56720 | 0.10919 |

${\beta}_{8}$ | ${\mathit{RH}}_{\mathit{t}}$ | 1.96325 | 0.16125 |

All parameters | 5.5039 | 6.193$\times {10}^{-7}$ |

**Table 5.**Descriptive statistics for PM${}_{10}$ by wind direction: Arboretum station (n—the number of observations; Mean—arithmetic mean; Median—median; St. dev.—standard deviation; Min—minimum; Max—maximum; ${Q}_{0.25}$—lower quartile; ${Q}_{0.75}$—upper quartile).

Degrees | n | Mean | Median | St. dev. | Min | Max | ${\mathit{Q}}_{0.25}$ | ${\mathit{Q}}_{0.75}$ |
---|---|---|---|---|---|---|---|---|

all | 1828 | 27.0 | 24.8 | 16.6 | 0.9 | 109.5 | 14.7 | 37.0 |

345–15 | 139 | 27.0 | 24.4 | 14.4 | 3.4 | 109.5 | 17.4 | 33.4 |

15–45 | 154 | 27.0 | 25.9 | 11.3 | 6.5 | 79.5 | 19.8 | 33.5 |

45–75 | 91 | 26.6 | 25.0 | 13.3 | 4.0 | 90.5 | 19.1 | 32.5 |

75–105 | 45 | 31.5 | 28.5 | 16.7 | 3.8 | 71.5 | 18.4 | 43.3 |

105–135 | 67 | 28.0 | 23.9 | 17.3 | 1.4 | 79.2 | 15.5 | 40.1 |

135–165 | 334 | 32.2 | 31.6 | 18.2 | 2.2 | 94.4 | 17.4 | 45.3 |

165–195 | 43 | 22.8 | 19.7 | 19.3 | 1.9 | 84.3 | 6.2 | 34.4 |

195–225 | 32 | 19.7 | 14.3 | 19.6 | 0.9 | 89.3 | 4.2 | 31.9 |

225–255 | 45 | 11.9 | 7.0 | 11.4 | 2.8 | 50.4 | 4.2 | 12.9 |

255–285 | 116 | 18.5 | 12.4 | 17.2 | 1.4 | 81.4 | 5.8 | 27.0 |

285–315 | 358 | 24.7 | 20.8 | 16.6 | 1.4 | 83.2 | 12.6 | 34.4 |

315–345 | 404 | 29.1 | 27.0 | 15.5 | 2.2 | 101.6 | 18.3 | 37.8 |

**Table 6.**Descriptive statistics for PM${}_{10}$ by wind direction: Černá Pole station (n—the number of observations; Mean—arithmetic mean; Median—median; St. dev.—standard deviation; Min—minimum; Max—maximum; ${Q}_{0.25}$—lower quartile; ${Q}_{0.75}$—upper quartile).

Degrees | n | Mean | Median | St. dev. | Min | Max | ${\mathit{Q}}_{0.25}$ | ${\mathit{Q}}_{0.75}$ |
---|---|---|---|---|---|---|---|---|

all | 1870 | 23.5 | 21.1 | 13.5 | 2.1 | 87.3 | 13.5 | 31.5 |

345–15 | 35 | 25.6 | 21.9 | 13.7 | 7.7 | 67.8 | 16.3 | 33.4 |

15–45 | 69 | 22.8 | 21.5 | 10.7 | 6.6 | 67.0 | 15.9 | 29.0 |

45–75 | 182 | 24.3 | 21.6 | 11.7 | 5.3 | 72.3 | 16.1 | 30.9 |

75–105 | 94 | 25.2 | 23.0 | 10.1 | 6.7 | 49.2 | 17.1 | 31.8 |

105–135 | 120 | 24.6 | 21.4 | 12.8 | 5.6 | 52.0 | 14.4 | 34.1 |

135–165 | 326 | 28.2 | 27.5 | 14.8 | 2.5 | 74.5 | 17.2 | 37.1 |

165–195 | 121 | 19.6 | 19.1 | 12.9 | 2.4 | 65.3 | 8.3 | 29.1 |

195–225 | 93 | 20.2 | 18.8 | 9.5 | 3.0 | 55.8 | 14.0 | 26.8 |

225–255 | 282 | 17.4 | 16.1 | 9.8 | 2.6 | 54.2 | 9.6 | 23.9 |

255–285 | 351 | 20.4 | 18.2 | 13.5 | 2.1 | 65.2 | 8.8 | 29.1 |

285–315 | 147 | 32.6 | 29.9 | 15.1 | 6.0 | 87.3 | 23.3 | 40.1 |

315–345 | 50 | 29.8 | 26.4 | 14.9 | 8.7 | 84.6 | 18.0 | 38.0 |

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**MDPI and ACS Style**

Neubauer, J.; Michálek, J.; Šilinger, K.; Firbas, P. Impacts of Built-Up Area Geometry on PM_{10} Levels: A Case Study in Brno, Czech Republic. *Atmosphere* **2020**, *11*, 1042.
https://doi.org/10.3390/atmos11101042

**AMA Style**

Neubauer J, Michálek J, Šilinger K, Firbas P. Impacts of Built-Up Area Geometry on PM_{10} Levels: A Case Study in Brno, Czech Republic. *Atmosphere*. 2020; 11(10):1042.
https://doi.org/10.3390/atmos11101042

**Chicago/Turabian Style**

Neubauer, Jiří, Jaroslav Michálek, Karel Šilinger, and Petr Firbas. 2020. "Impacts of Built-Up Area Geometry on PM_{10} Levels: A Case Study in Brno, Czech Republic" *Atmosphere* 11, no. 10: 1042.
https://doi.org/10.3390/atmos11101042