scholarly journals Evaluation of Relationship Between Air Pollutant Concentration and Meteorological Elements in Winter Months

2014 ◽  
Vol 22 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Andrzej Żyromski ◽  
Małgorzata Biniak-Pieróg ◽  
Ewa Burszta-Adamiak ◽  
Zenon Zamiar
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Winter Season ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Meteorological Elements

Abstract The paper presents the evaluation of the relation between meteorological elements and air pollutants’ concentrations. The analysis includes daily concentrations of pollutants and variation of meteorological elements such as wind speed, air temperature and relative humidity, precipitation and total radiation at four monitoring stations located in the province of Lower Silesia in individual months of the winter half-year (November–April, according to hydrological year classification) of 2005–2009. Data on air quality and meteorological elements came from the results of research conducted in the automatic net of air pollution monitoring conducted in the range of the State Environment Monitoring. The effect of meteorological elements on analysed pollutant concentration was determined using the correlation and regression analysis at significance level α < 0.05. The occurrence of maximum concentration of NO, NO2, NOX, SO2 and PM10 occurred in the coldest months during winter season (January, February and December) confirmed the strong influence of “low emission” on air quality. Among the meteorological factors assessed wind speed was most often selected component in step wise regression procedure, then air temperature, less air relative humidity and solar radiation. In the case of a larger number of variables describing the pollution in the atmosphere, in all analyzed winter seasons the most common set of meteorological elements were wind speed and air temperature.

scholarly journals COMPARAÇÃO ENTRE DADOS METEOROLÓGICOS OBTIDOS POR ESTAÇÕES CONVENCIONAIS E AUTOMÁTICAS NO ESTADO DO PIAUÍ, BRASIL*

Irriga ◽  
2017 ◽  
Vol 22 (2) ◽  
pp. 220-235
Author(s):  
Aureliano De Albuquerque Ribeiro ◽  
Aderson Soares De Andrade Júnior ◽  
Everaldo Moreira Da Silva ◽  
Marcelo Simeão ◽  
Edson Alves Bastos
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Atmospheric Pressure ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Average Relative Humidity ◽  
Operational Issues ◽  
Meteorological Elements

COMPARAÇÃO ENTRE DADOS METEOROLÓGICOS OBTIDOS POR ESTAÇÕES CONVENCIONAIS E AUTOMÁTICAS NO ESTADO DO PIAUÍ, BRASIL*  AURELIANO DE ALBUQUERQUE RIBEIRO1; ADERSON SOARES DE ANDRADE JÚNIOR2; EVERALDO MOREIRA DA SILVA3; MARCELO SIMEÃO4 E EDSON ALVES BASTOS2 1Doutorando em Engenharia Agrícola, Universidade Federal do Ceará, Av. Mister Hull, s/n - Pici, bloco 804, 60455-760, Fortaleza - CE, [email protected] Embrapa Meio-Norte, Teresina, PI, [email protected], [email protected] Professor Adjunto II da Universidade Federal do Piauí, Campus Professora Cinobelina Elvas, Bom Jesus, PI, [email protected] Mestre em Agronomia: Solos e Nutrição de Plantas, Universidade Federal do Piauí, Campus Professora Cinobelina Elvas, Bom Jesus, PI, [email protected]*Extraído da dissertação de mestrado do primeiro autor  1 RESUMOO registro de elementos climáticos é efetuado por estações meteorológicas convencionais e automáticas. Porém, por questões operacionais e de custo, as estações automáticas estão substituindo as convencionais. Contudo, para que as séries de dados dessas estações sejam únicas, há a necessidade de estudos comparativos entre as duas estações. O estudo teve como objetivo comparar dados meteorológicos obtidos por estações convencionais (EMC) e automáticas (EMA) em municípios do Estado do Piauí (Paulistana, Picos, São João do Piauí, Floriano, Parnaíba e Piripiri). Os elementos meteorológicos avaliados foram: temperaturas do ar máxima (°C) mínima (ºC) e média (ºC), umidade relativa média do ar (%), velocidade do vento a 10 m (m s-1), precipitação pluviométrica (mm) e pressão atmosférica média (hPa). As comparações dos dados foram feitas por meio dos seguintes indicadores estatísticos: precisão (R2), erro absoluto médio (EAM), coeficiente de correlação (r), índice de concordância de Willmott (d) e índice de confiança (c). Os melhores ajustes dos dados foram constatados para a precipitação e pressão atmosférica; intermediários, para a temperatura do ar, umidade relativa do ar média e os piores, para a velocidade do vento. A umidade relativa média do ar foi o elemento analisado que mostrou as maiores diferenças entre a EMC e a EMA. Palavras-chave: Agrometeorologia, elementos climáticos, sensores. RIBEIRO, A. A.; ANDRADE JÚNIOR, A. S.; SILVA, E.M.; SIMEÃO, M.; BASTOS, E.A.COMPARISON OF METEOROLOGICAL DATA RECORDED BY CONVENTIONAL AND AUTOMATIC STATIONS IN PIAUÍ STATE, BRAZIL   2 ABSTRACTClimatic elements are recorded by both conventional and automatic weather stations. However, due to cost and operational issues, automatic stations are replacing the conventional. So that  data sets from these stations are unique, there is a need for comparative studies between the two types of stations. The aim of this study was to compare meteorological data obtained by conventional and automatic stations in towns of the State of Piauí, Brazil (Paulistana, Picos, São João do Piauí, Floriano and Piripiri).The meteorological elements evaluated were: maximum (°C) minimum (°C) and average (°C) air temperature, average relative humidity (%), wind speed at 10 m (m s-1), rainfall (mm) and average atmospheric pressure (hPa). Data comparison was by the following statistical indicators: precision (R2), mean absolute error (EAM), Pearson correlation coefficient (r), Willmott’s index of agreement (d) and confidence index (c).  The best data adjustments were observed for rainfall and atmospheric pressure; intermediates for the air temperature, average relative humidity and worst for the wind speed.  The air average relative humidity was the analyzed element that showed the greatest differences between EMC and EMA. Keywords: Agrometeorology, meteorological elements, sensors 

scholarly journals Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China

2019 ◽  
Vol 17 (1) ◽  
pp. 136
Author(s):  
Wei Xue ◽  
Qingming Zhan ◽  
Qi Zhang ◽  
Zhonghua Wu
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Air Pollutant ◽  
Weekend Effect ◽  
Gaseous Pollutants ◽  
Statistical Point ◽  
Particulate Pollutants

High air pollution levels have become a nationwide problem in China, but limited attention has been paid to prefecture-level cities. Furthermore, different time resolutions between air pollutant level data and meteorological parameters used in many previous studies can lead to biased results. Supported by synchronous measurements of air pollutants and meteorological parameters, including PM2.5, PM10, total suspended particles (TSP), CO, NO2, O3, SO2, temperature, relative humidity, wind speed and direction, at 16 urban sites in Xiangyang, China, from 1 March 2018 to 28 February 2019, this paper: (1) analyzes the overall air quality using an air quality index (AQI); (2) captures spatial dynamics of air pollutants with pollution point source data; (3) characterizes pollution variations at seasonal, day-of-week and diurnal timescales; (4) detects weekend effects and holiday (Chinese New Year and National Day holidays) effects from a statistical point of view; (5) establishes relationships between air pollutants and meteorological parameters. The principal results are as follows: (1) PM2.5 and PM10 act as primary pollutants all year round and O3 loses its primary pollutant position after November; (2) automobile manufacture contributes to more particulate pollutants while chemical plants produce more gaseous pollutants. TSP concentration is related to on-going construction and road sprinkler operations help alleviate it; (3) an unclear weekend effect for all air pollutants is confirmed; (4) celebration activities for the Chinese New Year bring distinctly increased concentrations of SO2 and thereby enhance secondary particulate pollutants; (5) relative humidity and wind speed, respectively, have strong negative correlations with coarse particles and fine particles. Temperature positively correlates with O3.

scholarly journals Influence of Meteorological Parameters on Air Quality at Hashemite University, Jordan

2017 ◽  
Vol 12 (2) ◽  
pp. 211-221
Author(s):  
Sana’a Odata ◽  
Abu- Allabanb ◽  
Khitam Odibatb
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Negative Correlation ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Positive Correlation ◽  
Hashemite University

Four threshold air pollutants (SO2, NO, NO2, and O3) in addition to meteorological parameters were monitored at the Campus of the Hashemite University (HU) for two years (1/1/2012 through 30/12/013). Correlations between air pollution and meteorological parameters were derived. The results showed that O3 has a positive correlation with air temperature, wind speed and wind direction, but has a negative correlation with the relative humidity (RH). SO2 was found to have a negative correlation with the RH and wind speed, but positive correlation with air temperature. NO has negative correlation with air temperature, RH, and wind speed. And finally, NO2 has a negative correlation with RH and wind speed, but it has positive correlation with air temperature. Justify the reasons in brief with recommendations to improve the air quality

scholarly journals Impact of CO2 concentration on indoor air quality and correlation with relative humidity and indoor air temperature in school buildings in Serbia

2016 ◽  
Vol 20 (suppl. 1) ◽  
pp. 297-307 ◽  
Author(s):  
Ivan Lazovic ◽  
Zarko Stevanovic ◽  
Milena Jovasevic-Stojanovic ◽  
Marija Zivkovic ◽  
Milos Banjac
Keyword(s):  
Carbon Dioxide ◽  
Air Quality ◽  
Relative Humidity ◽  
Air Temperature ◽  
Indoor Air ◽  
Negative Impact ◽  
Winter Season ◽  
Carbon Dioxide Concentration ◽  
Average Concentration ◽  
Heating Season

Previous studies have shown that poorly ventilated classrooms can have negative impact on the health of children and school staff. In most cases, schools in Serbia are ventilated naturally. Considering their high occupancy, classroom air quality test determines the level of air pollution, after which it is possible to implement corrective measures. The research presented in this study was conducted in four schools which are located in different areas and have different architecture designs. Measurements in these schools have been performed during the winter (heating season) and spring (non-heating season) and the following results were presented: indoor air temperature, relative humidity and carbon dioxide concentration. These results show that the classroom average concentration of carbon dioxide often exceeds the value of 1500 ppm, during its full occupancy, which indicates inadequate ventilation. Measurement campaigns show that carbon dioxide concentration increased significantly from non-heating to heating season in three of the four schools. Analysis of measurements also determined high correlation between relative humidity and carbon dioxide concentration in all schools in winter season. This fact may constitute a solid basis for the fresh air supply strategy.

Research on air pollution system based on neural network

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Image Features ◽  
Air Pollutant ◽  
Pollution Monitoring ◽  
System Model ◽  
Process Data ◽  
Deep Convolutional Neural Networks ◽  
Air Pollution Monitoring ◽  
Neural Network Algorithm

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.

scholarly journals COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 290
Author(s):  
Akvilė Feiferytė Skirienė ◽  
Žaneta Stasiškienė
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Social Services ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rapid Spread ◽  
Air Quality Improvement ◽  
Environment Agency ◽  
Pollution Emissions ◽  
Air Pollution Emissions

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

Empirical Equations to Estimate Evapotranspiration in Mosul City

2020 ◽  
Vol 27 (4) ◽  
pp. 98-102
Author(s):  
Haqqi Yasin ◽  
Luma Abdullah
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Daily Data ◽  
Daily Sunshine ◽  
Measured Wind Speed

Average daily data of solar radiation, relative humidity, wind speed and air temperature from 1980 to 2008 are used to estimate the daily reference evapotranspiration in the Mosul City, North of Iraq. ETo calculator software with the Penman Monteith method standardized by the Food and Agriculture Organization is used for calculations. Further, a nonlinear regression approach using SPSS Statistics is utilized to drive the daily reference evapotranspiration relationships in which ETo is function to one or more of the average daily air temperature, actual daily sunshine duration, measured wind speed at 2m height and relative humidity

The Concentration Characteristics of PM10 and PM2.5 in Eight Cities of China in March, 2013

2013 ◽  
Vol 807-809 ◽  
pp. 20-23 ◽  
Author(s):  
Tao Sheng ◽  
Jian Wu Shi ◽  
Sen Lin Tian ◽  
Li Mei Bi ◽  
Hao Deng ◽  
...  
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Negative Correlation ◽  
Ambient Air ◽  
Quality Standard ◽  
Meteorological Factor ◽  
Urban Air ◽  
Ambient Air Quality ◽  
Ambient Air Quality Standard

According to the information of air quality which published by the urban air quality real-time publishing platform, the concentration characteristics of PM10 and PM2.5 were studied in Kunming (KM), Changsha (CS), Hangzhou (HZ), Shanghai (SH), Harbin (HEB), Beijing (BJ), Wuhan (WH) and Guangzhou (GZ). The results show that the concentrations of PM10 and PM2.5 exceeded the Ambient Air Quality Standard (GB3095-2012) in varying degrees in March, 2013. The concentrations of PM10 in Wuhan is the highest, reached 164μg/m3, exceeded the standard by 9.3%; the concentrations of PM2.5 is much higher in Wuhan, Changsha and Beijing, the average concentrations were 96μg/m3, 103μg/m3 and 110μg/m3, exceeded the standard by 28.0%, 37.3% and 46.7% respectively. The correlation of PM10 with PM2.5 in most of these cities was good in March. The correlation analysis of pollutant with meteorological factor in Hangzhou, Shanghai, Beijing and Guangzhou was also studied, the results show that the concentrations of PM10 and PM2.5 are weakly positive correlation with temperature in the four cities, negative correlation with relative humidity without Beijing, and negative correlation with wind speed.

scholarly journals Geostatistics applied to the environmental mapping of aviaries

2020 ◽  
Vol 24 (6) ◽  
pp. 409-414 ◽  
Author(s):  
Iug Lopes ◽  
Marcos V. da Silva ◽  
Juliana M. M. de Melo ◽  
Abelardo A. de A. Montenegro ◽  
Héliton Pandorfi
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Decision Making Process ◽  
Control Points ◽  
Internal Environment ◽  
Environmental Mapping ◽  
Critical Control Points ◽  
Meteorological Elements ◽  
Thermal Variability ◽  
Descriptive Statistical Analysis

ABSTRACT Spatial variability analysis of meteorological elements and precise identification of possible causes of thermal stress in poultry housing help producers in the decision making process. The objective of this study was to evaluate the internal environment of poultry houses in the downtime (sanitary void) and in the production phase, to characterize spatial thermal variability and to identify critical control points. The study was carried out in the Alluvial Valley of the Mimoso River, municipality of Pesqueira, PE, Brazil. The data of air temperature, wind speed and illuminance were recorded in November (spring season), at 155 points within each poultry facility (10 x 90 m), spaced in a 3.0 x 2.5 m grid and subjected to descriptive statistical analysis and geostatistics. There was a strong spatial dependence for the variables air temperature, wind speed and illuminance. The ranges obtained for the air temperature in the facilities were from 48.22 to 114.52 m, while for the wind speed and illuminance were less than 10 m, thus revealing the need for greater homogeneity of the studied variables and meeting of the thermal requirement of the poultry.

scholarly journals A review on Assessment of Air Pollution due to Vehicular Emission in Traffic Area

2018 ◽  
Vol 8 (02) ◽  
Author(s):  
Aneri A. Desai
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Ambient Air ◽  
Pollution Monitoring ◽  
Motor Vehicles ◽  
Monitoring And Control ◽  
Ambient Air Quality ◽  
Vehicular Emission ◽  
Rapid Urbanization ◽  
Air Pollution Monitoring

In Indian metropolitan cities, the extensive growth of the motor vehicles has resulted in the deterioration of environmental quality and human health. The concentrations of pollutants at major traffic areas are exceeding the permissible limits. Public are facing severe respiratory diseases and other deadly cardio-vascular diseases In India. Immediate needs for vehicular air pollution monitoring and control strategies for urban cities are necessary. Vehicular emission is the main source of deteriorating the ambient air quality of major Indian cities due to rapid urbanization. Total vehicular population is increased to 15 Lacks as per recorded data of Regional Transport Organization (RTO) till 2014-2015. This study is focused on the assessment of major air pollution parameters responsible for the air pollution due to vehicular emission. The major air pollutants responsible for air pollution due to vehicular emissions are PM10, PM2.5, Sox, Nox, HC, CO2 and CO and Other meterological parameters like Ambient temperature, Humidity, Wind direction and Wind Speed. Sampling and analysis of parameters is carried out according to National Ambient Air Quality Standards Guidelines (NAAQS) (2009) and IS 5128.

Sign in / Sign up

Export Citation Format

Share Document