scholarly journals Effect of Meteorological Conditions and Anthropogenic Factors on Air Concentrations of PM2.5 and PM10 Particulates on the Examples of the City of Kielce, Poland

2021 ◽  
Vol 13 (1) ◽  
pp. 1-11
Author(s):  
B. Szeląg ◽  
J. Studziński ◽  
M. Majewska
Keyword(s):  
Air Quality ◽  
Correlation Coefficient ◽  
Meteorological Conditions ◽  
Parametric Models ◽  
Air Purification ◽  
Anthropogenic Factors ◽  
Support Vector ◽  
Industrial Plants ◽  
Pm2.5 And Pm10 ◽  
Air Concentrations

The paper analyzes the influence of meteorological conditions (air temperature, wind speed, humidity, visibility) and anthropogenic factors (population in cities and in rural areas, road length, number of vehicles, emission of dusts and gases, coal consumption in industrial plants, number of air purification devices installed in industrial plants) on the concentration of PM2.5 and PM10 dusts in the air in the region of Kielce city in Poland. Spearman correlation coefficient was used to evaluate the relationship between the mentioned independent variables and air quality indicators. The calculated values of the correlation coefficient showed statistically significant relationships between air quality and the amount of installed air purification equipment in industrial plants. A statistically significant effect of the population in rural settlement units on the increase in air concentrations of PM2.5 and PM10 was also found, which proves the influence of the so-called low emission of pollutants on the air quality in the studied region. The analyses also revealed a statistically significant effect of road length on the decrease in PM2.5 and PM10 air content. This result indicates that a decrease in traffic intensity on particular road sections leads to an improvement in air quality. The analyses showed that despite the progressing anthropopression in the Kielce city region the air quality with respect to PM2.5 and PM10 content is improving. To verify the results obtained from statistical calculations, parametric models were also determined to predict PM2.5 and PM10 concentrations in the air, using the methods of Random Forests (RF), Boosted Trees (BT) and Support Vector Machines (SVM) for comparison purposes. The modelling results confirmed the conclusions that had been made based on previous statistical calculations.

scholarly journals A Multiple Kernel Learning Approach for Air Quality Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hong Zheng ◽  
Haibin Li ◽  
Xingjian Lu ◽  
Tong Ruan
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Arima Model ◽  
Multiple Kernel Learning ◽  
Parametric Models ◽  
Kernel Learning ◽  
Support Vector ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Multiple Kernel

Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted. In this paper, a novel multiple kernel learning (MKL) model that embodies the characteristics of ensemble learning, kernel learning, and representative learning is proposed to forecast the near future air quality (AQ). The centered alignment approach is used for learning kernels, and a boosting approach is used to determine the proper number of kernels. To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. Datasets acquired from a coastal city Hong Kong and an inland city Beijing are used to train and validate all the models. Experiments show that the MKL model outperforms the other models. Moreover, the MKL model has better forecast ability for high health risk category AQ.

scholarly journals Impacts of meteorological conditions on PM2.5 and PM10 pollution in Zhengzhou, China

2021 ◽  
Vol 257 ◽  
pp. 03025
Author(s):  
Rui Gao ◽  
Bairong Wang ◽  
Shunxiang Huang
Keyword(s):  
Air Quality ◽  
Relative Humidity ◽  
Meteorological Parameters ◽  
Meteorological Conditions ◽  
Backward Trajectory ◽  
Aerosol Pollution ◽  
Reduce Emissions ◽  
Pm2.5 And Pm10 ◽  
The Relationship ◽  
Local Emissions

Meteorological conditions play an important role in aerosol pollution. In this study, the relationships between wind, temperature, relative humidity, and aerosol concentrations (PM2.5 and PM10) in Zhengzhou from January 2016 to December 2017 were analysed. Backward trajectory model was also used to investigate the relationship between meteorological parameters and regional transport of pollutants. Significant seasonal variations can be observed in the time series of pollutants and wind, temperature and relative humidity. The simulation of backward trajectories indicated that pollutants from southeast is critical to the air quality in Zhengzhou, in addition to local emissions of pollutants. To improve the air quality in Zhengzhou, joint efforts to reduce emissions in both Zhengzhou and its southeast adjacent regions should be considered.

scholarly journals Effect of Air Purification Systems on Particulate Matter and Airborne Bacteria in Public Buses

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 55
Author(s):  
Jae Jung Lee ◽  
Hyemin Hwang ◽  
Suk Chan Hong ◽  
Jae Young Lee
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
South Korea ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
High Efficiency ◽  
Transport Systems ◽  
Air Purification ◽  
Airborne Bacteria ◽  
Pm2.5 And Pm10

The indoor air quality in public transport systems is a major concern in South Korea. Within this context, we investigated the effect of air purification systems on the indoor air quality of intercity buses, one of the most popular transport options in South Korea. Air purifiers were custom designed and equipped with high-efficiency particulate air (HEPA) filters to remove particulate matter and ultraviolet light-emitting diodes (UV-LEDs) to remove airborne bacteria. To investigate the effectiveness of the air purification systems, we compared concentrations of particulate matter (PM2.5 and PM10), airborne bacteria, and carbon dioxide (CO2) in six buses (three with air purification systems and three without) along three bus routes (BUS1, BUS2, BUS3) in Gyeonggi Province, South Korea, between 6 April and 4 May 2021. Compared to the buses without air purification, those with air purification systems showed 34–60% and 25–61% lower average concentrations of PM2.5 and PM10, respectively. In addition, buses with air purification systems had 24–78% lower average airborne bacteria concentrations compared to those without air purification systems (when measured after 30 min of initial purification).

VALORIZATION OF THE URBAN ENVIRONMENT OF SOUTH SERBIA FROM THE AIR QUALITY ASPECT

2018 ◽  
Vol 28 (4) ◽  
pp. 1329-1333
Author(s):  
Miodrag Šmelcerović
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Environment ◽  
Pollution Sources ◽  
Anthropogenic Sources ◽  
Anthropogenic Factors ◽  
Limit Values ◽  
Cause And Effect ◽  
Sources Of Pollution

The protection of the environment and people’s health from negative influences of the pollution of air as a medium of the environment requires constant observing of the air quality in accordance with international standards, the analysis of emission and imission of polluting matters in the air, and their connection with the sources of pollution. Having in mind the series of laws and delegated legislations which define the field of air pollution, it is necessary to closely observe these long-term processes, discovering cause-and-effect relationships between the activities of anthropogenic sources of emission of polluting matters and the level of air degradation. The relevant evaluation of the air quality of a certain area can be conducted if the level of concentration of polluting matters characteristic for the pollution sources of this area is observed in a longer period of time. The data obtained by the observation of the air pollution are the basis for creation of the recovery program of a certain area. Vranje is a town in South Serbia where there is a bigger number of anthropogenic pollution sources that can significantly diminish the air quality. The cause-and-effect relationship of the anthropogenic sources of pollution is conducted related to the analysis of systematized data which are in the relevant data base of the authorized institution The Institute of Public Health Vranje, for the time period between the year of 2012. and 2017. By the analysis of data of imission concentrations of typical polluting matters, the dominant polluting matters were determined on the territory of the town of Vranje, the ones that are the causers of the biggest air pollution and the risk for people’s health. Analysis of the concentration of soot, sulfur dioxide and nitrogen oxides indicates their presence in the air of Vranje town area in concentrations that do not exceed the permitted limit values annually. The greatest pollution is caused by the soot content in the air, especially in the winter period when the highest number of days with the values above the limit was registered. By perceiving the influence of natural and anthropogenic factors, it is clear that the concentration of polluting matters can be decreased only by establishing control over anthropogenic sources of pollution, and thus it can be contributed to the improvement of the air quality of this urban environment.

scholarly journals The On-Line Integrated Mesoscale Chemistry Model BOLCHEM

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 192
Author(s):  
Rita Cesari ◽  
Tony Christian Landi ◽  
Massimo D’Isidoro ◽  
Mihaela Mircea ◽  
Felicita Russo ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Transport Model ◽  
Horizontal Resolution ◽  
Integration Step ◽  
Chemistry Transport Model ◽  
European Environmental Agency ◽  
On Line ◽  
One Year ◽  
Pm2.5 And Pm10 ◽  
Monitoring Service

This work presents the on-line coupled meteorology–chemistry transport model BOLCHEM, based on the hydrostatic meteorological BOLAM model, the gas chemistry module SAPRC90, and the aerosol dynamic module AERO3. It includes parameterizations to describe natural source emissions, dry and wet removal processes, as well as the transport and dispersion of air pollutants. The equations for different processes are solved on the same grid during the same integration step, by means of a time-split scheme. This paper describes the model and its performance at horizontal resolution of 0.2∘× 0.2∘ over Europe and 0.1∘× 0.1∘ in a nested configuration over Italy, for one year run (December 2009–November 2010). The model has been evaluated against the AIRBASE data of the European Environmental Agency. The basic statistics for higher resolution simulations of O3, NO2 and particulate matter concentrations (PM2.5 and PM10) have been compared with those from Copernicus Atmosphere Monitoring Service (CAMS) ensemble median. In summer, for O3 we found a correlation coefficient R of 0.72 and mean bias of 2.15 over European domain and a correlation coefficient R of 0.67 and mean bias of 2.36 over Italian domain. PM10 and PM2.5 are better reproduced in the winter, the latter with a correlation coefficient R of 0.66 and the mean bias MB of 0.35 over Italian domain.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

Feature extraction method of pipeline signal based on parameter optimized vocational mode decomposition and exponential entropy

2021 ◽  
pp. 014233122110294
Author(s):  
Yina Zhou ◽  
Yong Zhang ◽  
Jingyi Lu ◽  
Fan Yang ◽  
Hongli Dong ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Working Conditions ◽  
Acoustic Signals ◽  
Normal Operation ◽  
Local Characteristic ◽  
Support Vector ◽  
K Value ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Exponential Entropy

Pipeline leakage is the main reason that affects normal operation of the pipeline. In this paper, a feature recognition method for pipeline acoustic signals based on vocational mode decomposition (VMD) and exponential entropy (EE) is investigated, which could extract the characteristics of pipeline signals and further accurately identify the pipeline acoustic signals under different working conditions. First, the VMD is used to decompose the collected acoustic signals into a number of mode components, during which process the optimal mode number (i.e., K-value) is determined by combining local characteristic scale decomposition (LCD) and correlation analysis methods. Then, the characteristic content of each mode component is analyzed with the help of the determined correlation coefficient (CC) threshold. If the correlation coefficient of a mode component is greater than the threshold, then the mode component is selected as the feature component. Subsequently, the EE values of the selected feature components are calculated to form the feature vectors corresponding to different kinds of pipeline signals. Finally, the feature vectors are input into support vector machine (SVM) to classify and recognize the different pipeline states. The experimental results demonstrate that the proposed method can identify the pipeline signals under different working conditions, and the recognition accuracy is up to [Formula: see text]. By analyzing and comparing with methods of EE-SVM, original data-SVM, VMD-singular spectrum entropy (SSE) and VMD-information entropy (IE), it is further verified that the proposed method is feasible and superior to the methods.

scholarly journals A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

2018 ◽  
Vol 8 (9) ◽  
pp. 1621 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Yong Ren ◽  
Gongbo Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Feature Space ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Acceleration Sensors ◽  
Reconstructed Signal ◽  
Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

scholarly journals Characterization of Air-Quality Degradation Episodes of PM10 in the Metropolitan Area of São Paulo and Their Relationship with Meteorological Conditions

2020 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
Maria C. Q. D. Oliveira ◽  
Luciana V. Rizzo ◽  
Anita Drumond
Keyword(s):  
Air Quality ◽  
Metropolitan Area ◽  
Sao Paulo ◽  
Meteorological Conditions ◽  
Pollutant Emissions ◽  
Maximum Temperature ◽  
São Paulo ◽  
Urban Centers ◽  
Duration Distribution ◽  
Air Quality Monitoring Stations

Air pollution is one of the main environmental problems in large urban centers, affecting people’s health and impacting quality of life. The Metropolitan Area of São Paulo (MASP) presents frequent exceedances of air-quality standards in inhalable particulate matter (PM10), a consequence of pollutant emissions modulated by meteorological conditions. This study aims to identify and characterize PM10persistent exceedance events (PEE) inthe MASP between 2005 and 2017, relating them to meteorological conditions. The criteria used to select the events were: (i) events that occurred in at least 50% of the air-quality monitoring stations chosen for this study and, (ii) among the events that met the first criterion, those with a duration equal to or greater than five days, which correspond to the 80% percentile of the event duration distribution. A total 71 persistent episodes of exceedance were selected. The results show that the exceedance of PM10 lasted up to 14 consecutive days and was predominant in the austral winter, accompanied by an increase in maximum temperature (T), a decrease in wind speed (WS) and relative humidity (RH), and a wind direction predominantly from the northwest during the peak concentration of the pollutant. On average, a concentration increase of 60% was observed at the peak of the PEE.

scholarly journals Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Akash Saxena ◽  
Shalini Shekhawat
Keyword(s):  
Support Vector Machine ◽  
Air Quality ◽  
Industrial Development ◽  
Learning Algorithm ◽  
Ambient Air ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Mathematical Framework ◽  
Primary Concern

With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air.

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