# Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters

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## Abstract

**:**

## 1. Introduction

#### 1.1. Motivation

#### 1.2. Data-Driven Air Pollutant Modeling

## 2. Materials

#### 2.1. Database

#### 2.2. Data Handling

#### 2.3. Environmental Conditions

## 3. Methods

#### 3.1. Data Pre-Processing

#### 3.2. Modeling

#### 3.3. Performance Metrics

## 4. Results

#### 4.1. Data Analysis

#### 4.2. Sensitivity Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ANN | Artificial neural network |

CMAQ | Community multiscale air quality |

CO | Carbon monoxide |

CPC | Condensation particle counter |

FFNN | Feed-forward neural network |

LASSO | Least absolute shrinkage and selection operator |

LSTM | Long short-term memory |

MAE | Mean absolute error |

MENA | Middle East and North Africa |

NNs | Neural networks |

NO${}_{2}$ | Nitrogen dioxide |

O${}_{3}$ | Ozone |

OPS | Optical particle sizer |

P | Absolute pressure |

PCC | Pearson correlation coefficients |

PM | Particulate matter |

PM${}_{10}$ | Particulate matter smaller than 10 $\mathsf{\mu}$m |

PM${}_{2.5}$ | Particulate matter smaller than 2.5 $\mathsf{\mu}$m |

PN | Particle number |

R${}^{2}$ | Coefficient of determination |

ReLU | Rectified linear unit |

RF | Precipitation |

RH | Relative humidity |

RMSE | Root mean squared error |

RNN | Recurrent neural network |

SMPS | Scanning Mobility Particle Sizer |

SO${}_{2}$ | Sulfur dioxide |

T | Temperature |

TDNN | Time-delay neural network |

UAM | Urban airshed model |

UFPs | Ultra-fine particles |

WD | Wind direction |

WHO | World Health Organization |

WS | Wind speed |

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**Figure 1.**PNsub, shown in subfigure (

**a**), and meteorological conditions, shown in subfigures (

**b**–

**f**), during the experiments. Red and blue colors are daily and hourly averaged.

**Figure 2.**PN number concentration histograms during the experiments for daily averaged data (

**left**) and hourly averaged data (

**right**).

**Figure 4.**Sensitivity analysis uses different combinations of meteorological variables as inputs for PN modeling.

**Figure 6.**Matrix plots: absolute Pearson correlation coefficients between measured variables for daily and hourly averaged data. (

**a**) Daily. (

**b**) Hourly.

**Figure 7.**Cross-correlation between PN data and meteorological parameters for daily and hourly averaged data. Different time lags are shown on the x-axis, whereas the y-axis represents normalized correlation coefficients (norm. cc). (

**a**) Daily. (

**b**) Hourly.

**Figure 8.**Performance metrics of daily modeling using FFNN (blue) and TDNN (red). The top, middle, and bottom sub-figures are R${}^{2}$, MAE, and RMSE, respectively.

**Figure 9.**Performance metrics of hourly modeling using FFNN (blue) and TDNN (red). The top, middle, and bottom sub-figures are R${}^{2}$, MAE, and RMSE, respectively.

**Figure 10.**Scatter plot between PN measurement and PN estimation using four measured meteorological variables (T, RH, P, and WS). (

**a**) Daily. (

**b**) Hourly.

**Figure 11.**Histograms of residual error between the reference instrument and PN estimation using four measured variables (T, RH, P, and WS).

**Figure 12.**The median of diurnal cycles calculated on different days for measured PN and modeled PN (No. 26).

**Figure 13.**The median of diurnal cycles calculated on workdays and weekends for measured PN and modeled PN (No. 26).

Performance Metrics | Formulation |
---|---|

Coefficient of Determination | ${\mathrm{R}}^{2}=1-\frac{{\sum}_{i=1}^{n}{({\widehat{y}}_{i}-{y}_{i})}^{2}}{{\sum}_{i=1}^{n}{({y}_{i}-\overline{y})}^{2}}$ |

Mean Absolute Error | $\mathrm{MAE}=\frac{{\sum}_{i=1}^{n}|{\widehat{y}}_{i}-{y}_{i}|}{n}$ |

Root Mean Squared Error | $\mathrm{RMSE}=\sqrt{\frac{{\sum}_{i=1}^{n}{({\widehat{y}}_{i}-{y}_{i})}^{2}}{n}}$ |

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**MDPI and ACS Style**

Zaidan, M.A.; Surakhi, O.; Fung, P.L.; Hussein, T. Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters. *Sensors* **2020**, *20*, 2876.
https://doi.org/10.3390/s20102876

**AMA Style**

Zaidan MA, Surakhi O, Fung PL, Hussein T. Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters. *Sensors*. 2020; 20(10):2876.
https://doi.org/10.3390/s20102876

**Chicago/Turabian Style**

Zaidan, Martha A., Ola Surakhi, Pak Lun Fung, and Tareq Hussein. 2020. "Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters" *Sensors* 20, no. 10: 2876.
https://doi.org/10.3390/s20102876