#
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

- Pope, C.A.; Dockery, D.W. Health effects of fine particulate air pollution: Lines that connect. J. Air Waste Manag. Assoc.
**2006**, 56, 709–742. [Google Scholar] [CrossRef] - Abrutzky, R.; Dawidowski, L.; Matus, P.; Lankao, P.R. Health effects of climate and air pollution in Buenos Aires: A first time series analysis. J. Environ. Prot.
**2012**, 3, 262–271. [Google Scholar] [CrossRef] [Green Version] - Restrepo, C.E.; Simonoff, J.S.; Thurston, G.D.; Zimmerman, R. Asthma hospital admissions and ambient air pollutant concentrations in New York City. J. Environ. Prot.
**2012**, 3, 1102–1116. [Google Scholar] [CrossRef] [Green Version] - EC (European Council). 1999/39/EC Directive of 22 April 1999 relating to limit values for sulphur dioxide, nitrogen dioxide and oxides of nitrogen, particulate matter and lead in ambient air. Off. J. Eur. Communities L
**1999**, 163, 0041–0060. [Google Scholar] - Li, Y.; Chen, Q.; Zhao, H.; Wang, L.; Tao, R. Variations in PM10, PM2.5 and PM1.0 in an Urban Area of the Sichuan Basin and Their Relation to Meteorological Factors. Atmosphere
**2015**, 6, 150–163. [Google Scholar] [CrossRef] [Green Version] - Dung, N.A.; Son, D.H.; Hanh, N.T.D.; Tri, D.Q. Effect of Meteorological Factors on PM10 Concentration in Hanoi, Vietnam. J. Geosci. Environ. Prot.
**2019**, 7, 137–150. [Google Scholar] [CrossRef] [Green Version] - Hörmann, S.; Pfeiler, B.; Stadlober, E. Analysis and prediction of particulate matter PM10 for the winter season in Graz. Austrian J. Stat.
**2005**, 34, 307–326. [Google Scholar] - Stadlober, E.; Hörmann, S.; Pfeiler, B. Quality and performance of a PM10 daily forecasting model. Atmos. Environ.
**2008**, 42, 1098–1109. [Google Scholar] [CrossRef] - Al-Hemoud, A.; Al-Dousari, A.; Al-Shatti, A.; Al-Khayat, A.; Behbehani, W.; Malak, M. Health Impact Assessment Associated with Exposure to PM10 and Dust Storms in Kuwait. Atmosphere
**2018**, 9, 6. [Google Scholar] [CrossRef] [Green Version] - Enkhjargal, A.; Oyun-Erdene, O.; Burmaajav, B.; Tsegmed, S.; Suvd, B.; Norolkhoosuren, B.; Unurbat, D.; Batbayar, J.; Narantuya, D.; Enkhtuya, P. Short Term Impact of Air Pollution on Asthma Admission in Ulaanbaatar. Occup. Dis. Environ. Med.
**2020**, 8, 64–78. [Google Scholar] [CrossRef] [Green Version] - Lelieveld, J.; Klingmüller, K.; Pozzer, A.; Pöschl, U.; Fnais, M.; Daiber, A.; Münzel, T. Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions. Eur. Heart J.
**2019**, 40, 1590–1596. [Google Scholar] [CrossRef] [Green Version] - Giannakis, E.; Kushta, J.; Giannadaki, D.; Georgiou, G.K.; Bruggeman, A.; Lelieveld, J. Exploring the economy-wide effects of agriculture on air quality and health: Evidence from Europe. Sci. Total Environ.
**2019**, 663, 889–900. [Google Scholar] [CrossRef] - Kushta, J.; Pozzer, A.; Lelieveld, J. Uncertainties in estimates of mortality attributable to ambient PM2.5 in Europe. Environ. Res. Lett.
**2018**, 13, 064029. [Google Scholar] [CrossRef] - Pozzer, A.; Bacer, S.; Sappadina, S.D.Z.; Predicatori, F.; Caleffi, A. Longterm concentrations of fine particulate matter and impact on human health in Verona, Italy. Atmos. Pollut. Res.
**2019**, 10, 731–738. [Google Scholar] [CrossRef] - Lelieveld, J.; Haines, A.; Pozzer, A. Age-dependent health risk from ambient air pollution: A modelling and data analysis of childhood mortality in middle-income and low-income countries. Lancet Planet. Health
**2018**, 2, 292–300. [Google Scholar] [CrossRef] [Green Version] - Lelieveld, J.; Klingmüller, K.; Pozzer, A.; Burnett, R.T.; Haines, A.; Ramanathan, V. Effects of fossil fuel and total anthropogenic emission removal on public health and climate. Proc. Natl. Acad. Sci. USA
**2019**, 116, 7192–7197. [Google Scholar] [CrossRef] [Green Version] - Wu, X.; Nethery, R.C.; Sabath, B.M.; Braun, D.; Dominici, F. Exposure to air pollution and COVID-19 mortality in the United States. medRxiv
**2020**, 42. [Google Scholar] [CrossRef] [Green Version] - Alam, M.S.; McNabola, A. Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis. J. Air Waste Manag. Assoc.
**2015**, 65, 628–640. [Google Scholar] [CrossRef] - Shahraiyni, H.T.; Sodoudi, S. Statistical Modeling Approaches for PM10 Prediction in Urban Areas, A Review of 21st-Century Studies. Atmosphere
**2016**, 7, 15. [Google Scholar] [CrossRef] [Green Version] - Liu, H.Y.; Schneider, P.; Haugen, R.; Vogt, M. Performance Assessment of a Low-Cost PM2.5 Sensor for a near Four-Month Period in Oslo, Norway. Atmosphere
**2019**, 10, 41. [Google Scholar] [CrossRef] [Green Version] - Bulejko, P.; Adamec, V.; Skeřil, R.; Schüllerová, B.; Bencko, B. Levels and Health Risk Assessment of PM10 Aerosol in Brno, Czech Republic. Cent. Eur. J. Public Health
**2017**, 25, 129–134. [Google Scholar] [CrossRef] - Pospisil, P.; Huzlik, J.; Licbinsky, R.; Spilacek, M. Dispersion Characteristics of PM10 Particles Identified by Numerical Simulation in the Vicinity of Roads Passing through Various Types of Urban Areas. Atmosphere
**2020**, 11, 454. [Google Scholar] [CrossRef] - Hrdličková, Z.; Michálek, J.; Kolář, M.; Veselý, V. Identification of factors affecting air pollution by dust aerosol PM10 in Brno City, Czech Republic. Atmos. Environ.
**2008**, 42, 8661–8673. [Google Scholar] [CrossRef] - Veselý, V.; Tonner, J.; Hrdličková, Z.; Michálek, J.; Kolář, M. Analysis of PM10 air pollution in Brno based on generalized linear model with strongly rank-deficient design matrix. Environmetrics
**2009**, 20, 676–698. [Google Scholar] [CrossRef] - Stadlober, E.; Hübnerova, Z.; Michálek, J.; Kolář, M. Forecasting of Daily PM10 Concentrations in Brno and Graz by Different Regression Approaches. Austrian J. Stat.
**2012**, 41, 287–310. [Google Scholar] [CrossRef] - Hübnerova, Z.; Michálek, J. Analysis of daily average PM10 prediction by generalized liner model in Brno. Czech Republic. Atmos. Pollut. Res.
**2014**, 5, 471–476. [Google Scholar] [CrossRef] - Newcombe, R.G. Two-Sided Confidence Intervals for the Single Proportion: Comparison of Seven Methods. Stat. Med.
**1998**, 17, 857–872. [Google Scholar] [CrossRef] - Passing, H.; Bablok, W. A New Biometrical Procedure for Testing the Equality of Measurements from Two Different Analytical Methods. Application of Linear Regression Procedures for Method Comparison Studies in Clinical Chemistry, Part I. J. Clin. Chem. Clin. Biochem.
**1983**, 21, 709–720. [Google Scholar] [CrossRef] - Searle, S.R. Linear Models; Wiley: New York, NY, USA, 1971. [Google Scholar]
- Fahrmeir, L.; Tutz, G. Generalized autoregressive linear model. In Multivariate Statistical Modelling Based on Generalized Linear Models; Springer: New York, NY, USA, 1994; pp. 23–24. [Google Scholar]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: https://www.R-project.org/ (accessed on 30 January 2020).
- Carslaw, D.C.; Ropkins, K. Openair—An R package for air quality data analysis. Environ. Model. Softw.
**2012**, 27–28, 52–61. [Google Scholar] [CrossRef] - Hyndman, R.; Athanasopoulos, G.; Bergmeir, C.; Caceres, G.; Chhay, L.; O’Hara-Wild, M.; Petropoulos, F.; Razbash, S.; Wang, E.; Yasmeen, F. Forecast: Forecasting Functions for Time Series and Linear Models. R package Version 8.12, Software, R package. 2020. Available online: http://pkg.robjhyndman.com/forecast (accessed on 10 February 2020).
- Manuilova, E.; Schuetzenmeister, A. mcr: Method Comparison Regression. R Package Version 1.2.1, Software, R Package. 2014. Available online: https://CRAN.R-project.org/package=mcr (accessed on 10 February 2020).
- Venables, W.N.; Ripley, B.D. Modern Applied Statistics with S, 4th ed.; Springer: New York, NY, USA, 2002. [Google Scholar]
- John Fox, J.; Weisberg, S. An R Companion to Applied Regression, 3rd ed.; Sage: Thousand Oaks, CA, USA, 2019; Available online: https://socialsciences.mcmaster.ca/jfox/Books/Companion/ (accessed on 15 February 2020).
- Mikuška, P.; Vojtěšek, M.; Křůmal, K.; Mikušková-Čampulová, M.; Michálek, J.; Večeřa, Z. Characterization and Source Identification of Elements and Water-Soluble Ions in Submicrometre Aerosols in Brno and Šlapanice (Czech Republic). Atmosphere
**2020**, 11, 688. [Google Scholar] [CrossRef]

**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