scholarly journals Seasonal Disparity in the Effect of Meteorological Conditions on Air Quality in China Based on Artificial Intelligence

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1670
Author(s):  
Yongli Zhang
Keyword(s):  
Air Quality ◽  
Wind Velocity ◽  
Meteorological Conditions ◽  
Seasonal Effects ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Beijing City ◽  
Atmospheric Humidity ◽  
Air Contamination ◽  
Maximum Wind

Air contamination is identified with individuals’ wellbeing and furthermore affects the sustainable development of economy and society. This paper gathered the time series data of seven meteorological conditions variables of Beijing city from 1 November 2013 to 31 October 2017 and utilized the generalized regression neural network optimized by the particle swarm optimization algorithm (PSO-GRNN) to explore seasonal disparity in the impacts of mean atmospheric humidity, maximum wind velocity, insolation duration, mean wind velocity and rain precipitation on air quality index (AQI). The results showed that in general, the most significant impacting factor on air quality in Beijing is insolation duration, mean atmospheric humidity, and maximum wind velocity. In spring and autumn, the meteorological diffusion conditions represented by insolation duration and mean atmospheric humidity had a significant effect on air quality. In summer, temperature and wind are the most significant variables influencing air quality in Beijing; the most important reason for air contamination in Beijing in winter is the increase in air humidity and the deterioration of air diffusion condition. This study investigates the seasonal effects of meteorological conditions on air contamination and suggests a new research method for air quality research. In future studies, the impacts of different variables other than meteorological conditions on air quality should be assessed.

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

Comparison of two air sampling methods to monitor operating room air quality and assessment of air quality in two operating rooms with different ventilation systems in the national hospital of Sri Lanka

2016 ◽  
Vol 12 (3) ◽  
Author(s):  
Tshokey Tshokey ◽  
Pranitha Somaratne ◽  
Suneth Agampodi
Keyword(s):  
Air Quality ◽  
Sri Lanka ◽  
Operating Room ◽  
Ventilation System ◽  
Surgical Site Infections ◽  
National Hospital ◽  
Air Contamination ◽  
Acceptable Method ◽  
The Difference ◽  
Laminar Air Flow

Air contamination in the operating room (OR) is an important contributor for surgical site infections. Air quality should be assessed during microbiological commissioning of new ORs and as required thereafter. Despite many modern methods of sampling air, developing countries mostly depended on conventional methods. This was studied in two ORs of the National Hospital of Sri Lanka (NHSL) with different ventilation system; a conventional ventilation (CV) and a laminar air flow (LAF). Both ORs were sampled simultaneously by two different methods, the settle plate and sampler when empty and during use for a defined time period. Laboratory work was done in the Medical Research Institute. The two methods of sampling showed moderate but highly significant correlation. The OR with CV was significantly more contaminated than LAF when empty as well as during use by both methods. Overall, the difference in contamination was more significant when sampled by the sampler. Differences in contamination in empty and in-use ORs were significant in both ORs, but significance is less in LAF rooms. The consistent and significant correlation between settle plate and sampler showed that the settle plate is an acceptable method. The LAF theatre showed less contamination while empty and during use as expected. Air contamination differences were more significant when sampled with sampler indicating that it is a more sensitive method. Both CV and LAF ORs of the NHSL did not meet the contamination standards for empty theatres but met the standards for in-use indicating that the theatre etiquette was acceptable.

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 Meteorological controls on atmospheric particulate pollution during hazard reduction burns

2017 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Melissa Anne Hart ◽  
Ningbo Jiang
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Meteorological Conditions ◽  
Atmospheric Conditions ◽  
Hazard Reduction ◽  
Particulate Pollution ◽  
Fine Particulate ◽  
Projected Changes ◽  
Sydney Australia

Abstract. Internationally, severe wildfires are an escalating problem likely to worsen given projected changes to climate. Hazard reduction burns (HRB) are used to suppress wildfire occurrences, but they generate considerable emissions of atmospheric fine particulate matter, which depending upon prevailing atmospheric conditions, can degrade air quality. Our objectives are to improve understanding of the relationships between meteorological conditions and air quality during HRBs in Sydney, Australia. We identify the primary meteorological covariates linked to high PM2.5 pollution (particulates

scholarly journals Air Quality Improvement in a Megacity: Implications from 2015 Beijing Parade Blue Pollution-Control Actions

2016 ◽  
Author(s):  
Wen Xu ◽  
Wei Song ◽  
Yangyang Zhang ◽  
Xuejun Liu ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Air Quality ◽  
Control Period ◽  
Emission Control ◽  
Monitoring Network ◽  
Control Measures ◽  
Integrated Analysis ◽  
Beijing City ◽  
Air Quality Improvement ◽  
Emission Control Measures

Abstract. The implementation of strict emission control measures in Beijing and surrounding regions during the 2015 China Victory Day Parade provided a valuable opportunity to investigate related air quality improvements in a megacity. We measured NH3, NO2 and PM2.5 at multiple sites in and outside Beijing and summarized concentrations of PM2.5, PM10, NO2, SO2 and CO in 291 cities across China from a national urban air quality monitoring network between August and September 2015. Consistently significant reductions of 12–35 % for NH3 and 33–59 % for NO2 in different areas of Beijing city during the emission control period (referred to as the Parade Blue period) were observed compared with measurements in the pre- and post-Parade Blue periods without emission controls. Average NH3 and NO2 concentrations at sites near traffic were strongly correlated and showed positive and significant responses to traffic reduction measures, suggesting that traffic is an important source of both NH3 and NOx in urban Beijing. Daily concentrations of PM2.5 and secondary inorganic aerosol (sulfate, ammonium, and nitrate) at the urban and rural sites both decreased during the Parade Blue period. Concentrations of PM2.5, PM10, NO2, SO2 and CO from the national city-monitoring network showed the largest decrease (34–72 %) in Beijing, a smaller decrease (1–32 %) in North China (excluding Beijing), and an increase (6–16 %) in other regions of China during the emission control period. Integrated analysis of modeling and monitoring results demonstrated that emission control measures made a major contribution to air quality improvement in Beijing compared with a minor contribution from favorable meteorological conditions during the Parade Blue period. These results show that controls of secondary aerosol precursors (NH3, SO2 and NOx) locally and regionally are key to curbing air pollution in Beijing and probably in other mega cities worldwide.

scholarly journals PREDICTION OF OZONE (O3) VALUES USING SUPPORT VECTOR REGRESSION METHOD

2021 ◽  
Vol 7 (4) ◽  
pp. 81-88
Author(s):  
Chasandra Puspitasari ◽  
Nur Rokhman ◽  
Wahyono
Keyword(s):  
Air Quality ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Motor Vehicles ◽  
Primary Data ◽  
Series Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Air Quality Prediction

A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.

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 Research on maintenance spare parts requirement prediction based on LSTM recurrent neural network

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 618-627
Author(s):  
Weixing Song ◽  
Jingjing Wu ◽  
Jianshe Kang ◽  
Jun Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Spare Parts ◽  
Performance Comparison ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Particle Swarm Algorithm

Abstract The aim of this study was to improve the low accuracy of equipment spare parts requirement predicting, which affects the quality and efficiency of maintenance support, based on the summary and analysis of the existing spare parts requirement predicting research. This article introduces the current latest popular long short-term memory (LSTM) algorithm which has the best effect on time series data processing to equipment spare parts requirement predicting, according to the time series characteristics of spare parts consumption data. A method for predicting the requirement for maintenance spare parts based on the LSTM recurrent neural network is proposed, and the network structure is designed in detail, the realization of network training and network prediction is given. The advantages of particle swarm algorithm are introduced to optimize the network parameters, and actual data of three types of equipment spare parts consumption are used for experiments. The performance comparison of predictive models such as BP neural network, generalized regression neural network, wavelet neural network, and squeeze-and-excitation network prove that the new method is effective and provides an effective method for scientifically predicting the requirement for maintenance spare parts and improving the quality of equipment maintenance.

scholarly journals The Sources of Moisture in the Sand Dunes – The Example of the Western Sahara Dune Field

2014 ◽  
Vol 33 (3) ◽  
pp. 199-204 ◽  
Author(s):  
Elwira Żmudzka ◽  
Dariusz Woronko ◽  
Maciej Dłużewski
Keyword(s):  
Surface Layer ◽  
Moisture Content ◽  
Wind Velocity ◽  
Sand Dune ◽  
Sand Dunes ◽  
Shear Velocity ◽  
Meteorological Conditions ◽  
Aeolian Transport ◽  
Western Sahara ◽  
Dune Field

Abstract Climatic and meteorological conditions may limit the aeolian transport within barchans. An explanation of that issue was the main goal of the investigation held in Western Sahara dune fields located around Tarfaya and Laâyoune. Particular attention was paid to the factors causing the moisture content rising of the sand dune surface layer, which could influence the wind threshold shear velocity in the aeolian transport. The wetted surface layer of sand, when receiving moisture from precipitation or suspensions, reduces the aeolian transport, even in case of wind velocity above 4-5 m s-1. Fog and dew condensation does not affect the moisture of deeper sand layers, what occurs after rainfall.

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