Characterization of Urban New Particle Formation in Amman—Jordan

Titanium Dioxide Promotes New Particle Formation: A Smog Chamber Study

TiO₂ is a widely used material in building coatings. Many studies have revealed that TiO₂ promotes the heterogeneous oxidation of SO₂ and the subsequent sulfate formation. However, whether and how much TiO₂ contributes to the gaseous H₂SO₄ and subsequent new particle formation (NPF) still remains unclear. Herein, we used a 1 m³ quartz smog chamber to investigate NPF in the presence of TiO₂. The experimental results indicated that TiO₂ could greatly promote NPF. The increases in particle formation rate (J) and growth rate due to the presence of TiO₂ were quantified, and the promotion effect was attributed to the production of gaseous H₂SO₄. The promotion effect of TiO₂ on SO₂ oxidation and subsequent NPF decreased gradually due to the formation of surface sulfate but did not disappear completely, instead partly recovering after washing with water. Moreover, the promotion effect of TiO₂ on NPF was observed regardless of differences in RH, and the most significant promotion effect of TiO₂ associated with the strongest NPF occurred at an RH of 20%. Based on the experimental evidence, the environmental impact of TiO₂ on gaseous H₂SO₄ and particle pollution in urban areas was estimated.

Particle number size distribution and new particle formation in Xiamen, the coastal city of Southeast China in wintertime

New particle formation (NPF) has a great impact on regional and global climate, air quality and human health. This study uses a Scanning Mobility Particle Sizer (SMPS) for simultaneous measurement of particle number size distribution (PNSD) in wintertime to investigate NPF in the coastal city of Xiamen. The mean particle number concentration, surface area concentration and volume concentration were 7.25 × 10³ cm^-3, 152.54 μm² cm^-3, and 4.03 μm³ cm^-3, respectively. Particle number concentration was mainly influenced by the nucleation mode and the Aitken mode, whereas the main contributor to particle surface area concentration and volume concentration was accumulation mode particles. The frequency of NPF events occurred was around 41.4% in December 2019. The typical growth rates of new formed particles were 1.41-2.54 nm h^-1, and the observed formation rates were 0.49-1.43 cm^-3 s^-1. A comparative analysis of conditions between event and non-event days was performed. The results emphasized that air temperature, UV radiation and relative humidity were the most decisive meteorological factors, and NPF events usually occurred under clean atmospheric conditions with low PM concentrations. Although condensation sink was high when NPF event occurred, the level of SO₂ and O₃ concentration was also high.

Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm

The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the forecasting accuracy based on the selection of an appropriate time-lag value by applying a comparative study between three methods. These methods include a statistical approach using auto correlation function, a well-known machine learning technique namely Long Short-Term Memory (LSTM) along with a heuristic algorithm to optimize the choosing of time-lag value, and a parallel implementation of LSTM that dynamically choose the best prediction based on the optimal time-lag value. The methods were applied to an experimental data set, which consists of five meteorological parameters and aerosol particle number concentration. The performance metrics were: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared. The investigation demonstrated that the proposed LSTM model with heuristic algorithm is the superior method in identifying the best time-lag value.

Regional New Particle Formation over the Eastern Mediterranean and Middle East

Atmospheric new particle formation (NPF) events taking place over large distances between locations, featuring similar characteristics, have been the focus of studies during the last decade. The exact mechanism which triggers NPF still remains indefinable, so are the circumstances under which simultaneous occurrence of such events take place in different environments, let alone in environments which are parted by over 1200 km. In this study, concurrent number size distribution measurements were conducted in the urban environments of Athens (Greece) and Amman (Jordan) as well as the regional background site of Finokalia, Crete, all located within a distance of almost 1300 km for a 6-month period (February–July 2017). During the study period Athens and Finokalia had similar occurrence of NPF (around 20%), while the occurrence in Amman was double. When focusing on the dynamic characteristics at each site, it occurs that formation and growth rates at Amman are similar to those at Finokalia, while lower values in Athens can be ascribed to a higher pre-existing particle number at this urban site. By comparing common NPF events there are 5 concomitant days between all three sites, highly related to air masses origin. Additionally, for another 19 days NPF takes place simultaneously between Finokalia and Amman, which also share common meteorological characteristics, adding to a total of 60% out of 41 NPF events observed at Finokalia, also simultaneously occurring in Amman.

Observations of new particle formation, modal growth rates, and direct emissions of sub-10 nm particles in an urban environment

Ultrafine particles with diameters less than 100 nm suspended in the air are a topic of interest in air quality and climate sciences. Sub-10 nm particles are of additional interest due to their health effects and contribution to particle growth processes. Ambient measurements were carried out at North Carolina State University in Raleigh, NC between April to June 2019 and November 2019 to May 2020 to investigate the temporal variability of size distribution and number concentration of ultrafine particles. A mobile lab was deployed between March and May 2020 to characterize the spatial distribution of sub-10 nm particle number concentration. New particle formation and growth events were observed regularly. Also observed were direct emissions of sub-10 nm particles. Analysis against meteorological variables, gas-phase species, and particle concentrations show that the sub-10nm particles dominated number concentration during periods of low planetary boundary layer height, low solar radiation, and northeast winds. The spatial patterns observed during mobile deployments suggest that multiple temporally stable and spatially confined point sources of sub-10 nm particles are present within the city. These sources likely include the campus utility plants and the Raleigh-Durham International Airport. Additionally, the timing of data collection allowed for investigation of variations in the urban aerosol number size distribution due to reduced economic activity during the COVID-19 pandemic.

Efficient data preprocessing, episode classification, and source apportionment of particle number concentrations

Number concentration is an important index to measure atmospheric particle pollution. However, tailored methods for data preprocessing and characteristic and source analyses of particle number concentrations (PNC) are rare and interpreting the data is time-consuming and inefficient. In this method-oriented study, we develop and investigate some techniques via flexible conditions, C++ optimized algorithms, and parallel computing in R (an open source software for statistics and graphics) to tackle these challenges. The data preprocessing methods include deletions of variables and observations, outlier removal, and interpolation for missing values (NA). They do better in cleaning data and keeping samples and generate no new outliers after interpolation, compared with previous methods. Besides, automatic division of PNC pollution events based on relative values suites PNC properties and highlights the pollution characteristics related to sources and mechanisms. Additionally, basic functions of k-means clustering, Principal Component Analysis (PCA), Factor Analysis (FA), Positive Matrix Factorization (PMF), and a newly-introduced model NMF (Non-negative Matrix Factorization) were tested and compared in analyzing PNC sources. Only PMF and NMF can identify coal heating and produce more explicable results, meanwhile NMF apportions more distinctly and runs 11-28 times faster than PMF. Traffic is interannually stable in non-heating periods and always dominant. Coal heating's contribution has decreased by 40%-86% in recent 5 heating periods, reflecting the effectiveness of coal burning control.

An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm—An Application for Aerosol Particle Number Concentrations

Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques—recurrent neural networks (RNN), heuristic algorithm and ensemble learning—to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants—Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network—with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model’s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions.

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

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R 2 = 0.77 ) and TDNN for hourly averaged data (with R 2 = 0.66 ) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters.