scholarly journals Air quality in cities of the extreme south of Brazil

2020 ◽  
Vol 15 ◽  
pp. 61-67
Author(s):  
F.M.R. Silva Júnior ◽  
L.C. Honscha ◽  
R.L. Brum ◽  
P.F. Ramires ◽  
R.A. Tavella ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Scientific Information ◽  
Meteorological Parameter ◽  
Rio Grande ◽  
Temporal Analysis ◽  
Atmospheric Parameter ◽  
Sources Of Pollution ◽  
South Of Brazil

The region comprised of cities located in the extreme south of Brazil has numerous potential sources of pollution, such as industries, mining and agricultural activities. Despite this, they do not have detailed scientific information regarding air quality. The present study aimed to evaluate air quality in nine municipalities in the extreme south of Brazil, based on the monitoring of six pollutants (O3 , NO2, SO2, PM2.5, PM10 and CO) present in Brazilian environmental legislation and the relationship of these pollutants with meteorological parameters. Information on air pollutants and meteorological parameters was collected from satellite data from the European Centre for Medium-Range Weather Forecasts “Copernicus Atmospheric Monitoring Service”, extracted using The Wealther Channel (IBM, USA) during the period ranged from April 25, 2020 to July 4, 2020 in Rio Grande, Pelotas, Bagé, Candiota, Hulha Negra, Pedras Altas, Aceguá and Herval. The concentration of pollutants was below Brazilian limits, with the exception of a single episode in the municipality of Rio Grande. Temperature was the meteorological parameter most correlated with air pollutants, except for SO2, but in general, all pollutants correlated (positive or negative) with at least one atmospheric parameter. Finally, the composition of air pollutants in each municipality seems to be related to its local economic activity. We encourage the continuity of studies in the region aiming at a complete temporal analysis that encompasses all seasons.

scholarly journals Modelling in Human Biometeorology: Spatial-Temporal Analysis of Thermal Indices

2021 ◽  
Vol 8 (1) ◽  
pp. 28
Author(s):  
Andreas Matzarakis ◽  
Marcel Gangwisch ◽  
Tim Herbert
Keyword(s):  
Meteorological Parameters ◽  
Meteorological Parameter ◽  
Temporal Analysis ◽  
Wave Radiation ◽  
Thermal Indices ◽  
Spatial And Temporal Scales ◽  
Mitigation And Adaptation ◽  
Radiation Fluxes ◽  
Scale Models ◽  
Radiant Temperature

The issue of the quantification of thermal comfort or heat stress on humans is in vogue nowadays. This is evident for indices, which are trying to quantify these effects. Most known indices are PET, modified PET, SET*, PT and UTCI. All thermal indices require the same thermo-physiological and meteorological parameters. Air temperature, air humidity, wind speed, and short and long wave radiation fluxes in terms of mean radiant temperature are the required meteorological parameters. For human thermo-physiology, information about heat production and clothing are required. The meteorological parameters have to be available in appropriate spatial and temporal scales depending on the target and the specific issues demanded. The appropriate spatial and temporal resolution data cannot only be delivered by measurement stations. Meso and micro scale models, which compute meteorological parameter and thermal indices, can be helpful in the development of mitigation and adaptation strategies in the era of climate change.

Changes of air pollutants’ concentrations in selected Romanian cities during the pandemic year 2020

2021 ◽  
Author(s):  
Gabriela Iorga ◽  
George-Bogdan Burghelea
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
European Commission ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Dust Particles ◽  
Spatial And Temporal Variation ◽  
Pollution Level ◽  
Data Set ◽  
Air Mass

<p>Present research contributes to scientific knowledge concerning spatial and temporal variation of major air pollutants with high resolution at the country scale bringing statistical information on concentrations of NOx, O<sub>3</sub>, CO, SO<sub>2</sub> and particulate matter with an aerodynamic diameter below 10 μm (PM<sub>10</sub>) and below 2.5 μm (PM<sub>2.5</sub>) during the pandemic year 2020 using an observational data set from the Romanian National Air Quality Network in seven selected cities spread out over the country. These cities have different level of development, play regional roles, might have potential influence at European scale and they are expected to be impacted by different pollution sources. Among them, three cities (Bucharest, Brașov, Iași) appear frequently on the list of the European Commission with reference to the infringement procedure that the European Commission launched against Romania in the period 2007-2020 regarding air quality.</p><p>Air pollutant data was complemented with local meteorological parameters at each site (atmospheric pressure, relative humidity, temperature, global solar radiation, wind speed and direction). Statistics of air pollutants provide us with an overview of air pollution in main Romanian cities.  Correlations between meteorological parameters and ambient pollutant levels were analyzed. Lowest air pollution levels were measured during the lockdown period in spring, as main traffic and non-essential activities were severely restricted. Among exceptions were the construction activities that were not interrupted. During 2020, some of selected cities experienced few pollution episodes which were due to dust transport from Sahara desert. However, in Bucharest metropolitan area, some cases with high pollution level were found correlated with local anthropogenic activity namely, waste incinerations. Air mass origins were investigated for 72 hours back by computing the air mass backward trajectories using the HYSPLIT model. Dust load and spatial distribution of the aerosol optical depth with BSC-DREAM8b v2.0 and NMBM/BSC-Dust models showed the area with dust particles transport during the dust events.</p><p>The obtained results are important for investigations of sources of air pollution and for modeling of air quality.</p><p><strong> </strong></p><p><strong>Acknowledgment:</strong></p><p>The research leading to these results has received funding from the NO Grants 2014-2021, under Project contract no. 31/2020, EEA-RO-NO-2019-0423 project. NOAA Air Resources Laboratory for HYSPLIT transport model, available at READY website https://www.ready.noaa.gov  and the Barcelona dust forecast center for BSC-DREAM8b and NMBM/BSC-Dust models, available at:  https://ess.bsc.es/bsc-dust-daily-forecast are also acknowledged. The data regarding ground-based air pollution and meteorology by site was extracted from the public available Romanian National Air Quality Database, www.calitateaer.ro.</p>

scholarly journals A Survey On Air Quality Prediction Using Traditional Statistics Method

2020 ◽  
pp. 942-946
Author(s):  
S. Karthikeyani ◽  
S. Rathi
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Moving Average ◽  
Quality Prediction ◽  
Atmospheric Air ◽  
Air Quality Prediction ◽  
Naive Method ◽  
Auto Regressive ◽  
Weighted Moving Average

Air pollution is the release of pollutants into the atmospheric air which are harmful to human health and the planet as a whole. Car emissions, dust, pollen, chemicals from factories and mold spores may be suspended as a particle. In this survey, the analyzes are made revolving on air quality prediction using the traditional statistics method. The prediction using air pollutants are PM2.5, PM10, NO2, NOx, NO, SO2, CO, O3 and meteorological parameters such as Absolute Temparathure(AT) and Relative Humidity(RH). In this comparison experiments, common predicted algorithms are Naive Method, Auto-Regressive Integrated Moving Average(ARIMA), Exponentially Weighted Moving Average(EWMA), Linear Regression(LR), LSTM model, Prophet Model are analyzed.

scholarly journals Temporal analysis of air pollutants in the most affected monsoon region

2021 ◽  
Vol 23 (1) ◽  
pp. 229
Author(s):  
Omar Kairan ◽  
Nur Nasehah Zainudin ◽  
Nurul Hasya Mohd Hanafiah ◽  
Nur Emylia Arissa Mohd Jafri ◽  
Fukayhah Fatiha @Suhami ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Monsoon Season ◽  
Secondary Data ◽  
Ambient Air ◽  
Temporal Analysis ◽  
Quality Standard ◽  
Temporal Variations ◽  
Air Pollutant

Air pollution has become an issue at all rates in the world. In Malaysia, there is a system is known as air quality index (API) used to indicate the overall air quality in the country where the air pollutants include or the new ambient air quality standard are sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and particulate matter with size less than 10 (PM10). The concentration levels of the air pollutants were said to be affected by the monsoon changes. Therefore, this study is conducted to examine the existence of temporal variations of each air pollutant then identify the differences of each air pollutants concentration in temporal variations. This study uses secondary data where data that has been retrieved from the Department of Environment (DOE) where it is data of air pollution specifically for Kota Bharu, kelantan records. Hierarchical agglomerative cluster analysis was conducted to group monthly air quality. As a conclusion, the study can conclude that the five air pollutants grouped into several different monthly clusters mostly representing the two main monsoon seasons. Mostly air pollutant varied accordingly towards the monsoon season. During the southwestern monsoon, air pollutant concentration tends to higher compare to the northeastern monsoon with mostly due to meteorological factors.

scholarly journals Analysis of Air Quality Impacts on Human Health Using the Geoinformatics Application: Chiang Rai Province

2017 ◽  
pp. 25-32
Author(s):  
Anuttara Hongthong ◽  
Yanasinee Suma ◽  
Nittaya Pasukphun ◽  
Vivat Keawdounglek
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Relative Humidity ◽  
Air Pollutants ◽  
Ambient Air ◽  
Temporal Analysis ◽  
City Centre ◽  
Pollution Dispersion ◽  
Public Health Office ◽  
Lower Temperature

This research aims to study air pollution dispersion in Chiang Rai Province, Thailand. The relationship between air pollutants, meteorology and population health were considered. The levels of air pollutants were used to establish a spatial and temporal analysis by Inverse Distance Weighted (IDW) interpolation from Geographic Information Systems (GIS), involved with occurrences of disease cases in the study area. The average monthly air pollution data were collected from Thailand’s Pollution Control Department and data on respiratory disease were collected from Chiang Rai Provincial Public Health Office during 2011 to 2014. The results indicated that monthly average PM10 concentrations started to rise from December to April. PM10 concentrations peaked during the hot season of every year, when open burning is prac-ticed. During this period, PM10 levels exceeded Thailand’s national ambient air quality standardsof 120 μg m-3. Accumulative influenza and pneumonia cases in Chiang Rai Province were very high in Chiang Rai city centre. The spatial temperature distribution map showed higher incidence of cases of influenza and pneumonia throughout the lower temperature area of Chiang Rai city centre. Influenza was affected by PM10, rainfall, relative humidity, and temperature, according to the following correlation ratios: 0.8217, 0.8842, 0.9375 and 0.8775, respectively. The incidence of pneumonia was affected by rainfall, relative humidity and temperature following the correlation ratios 0.7746, 0.7621 and 0.9684, respectively. Whereas PM10 was low associated with pneumonia as a significant ratio was 0.6079. Pneumonia incidence decreased when rainfall and temperature decreased, and increased when relative humidity increased.

scholarly journals NGHIÊN CỨU ỨNG DỤNG MẠNG TRẠNG THÁI PHẢN HỒI ĐỂ DỰ BÁO CHẤT LƯỢNG KHÔNG KHÍ

2016 ◽  
Vol 54 (1) ◽  
pp. 54 ◽  
Author(s):  
Mac Duy Hung ◽  
Nghiem Trung Dung
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Air Pollutant ◽  
Training Data ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Multilayer Perception ◽  
Testing Data ◽  
Better Than

A study on the application of Echo State Network (ESN) for the forecast of air quality in Hanoi for a period of seven days, which is based on the nonlinear relationships between the concentrations of an air pollutant to be forecasted and meteorological parameters, was conducted. Three air pollutants being SO2, NO2 and PM10 were selected for this study. Training data and testing data were extracted from the database of Lang air quality monitoring station, Hanoi, from 2003 to 2009. Values forecasted by ESN are compared with those by MLP (Multilayer Perception). Results shown that, in almost experiments, the performance of ESN is better than that of MLP in terms of the values and the correlation of concentration trends. The averages of RMSE of ESN and MLP for SO2 are 5.9 ppb and 6.9 ppb, respectively. For PM10, the accuracy of ESN is 83.8% with MAE of 53.5 μg/m3, while the accuracy of MLP is only 77.6% with MAE of 68.2 μg/m3. For NO2, the performance of ESN and MLP is similar; the accuracy of both models is in the range of 60% to 72.7%. These suggest that, ESN is a novel and feasible approach to build the air forecasting model. Keywords: Forecast, air quality, ESN, MLP, ANN, Hanoi, Vietnam.

scholarly journals Comparative study on air quality status in Golden Quadrilateral cities before and during the COVID-19 lock down period.

2021 ◽  
Vol 23 (06) ◽  
pp. 1-10
Author(s):  
Geeta Singh ◽  
◽  
Ayushi Jha ◽  
Rashmi Kumari ◽  
Vishal Kumar Singh ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
World Economy ◽  
Meteorological Parameters ◽  
Premature Mortality ◽  
Health Impact ◽  
Human Action ◽  
Quality Parameters ◽  
Pollution Level

The COVID-19 pandemics have affected every aspect of the human race and the world economy. The disease has been contaminated in almost every part of India. A threat for poor standards induced premature mortality from cardiovascular disease and respiratory diseases. Amongst the huge-reaching implications of the continuing COVID-19 outbreak, a significant enhancement in air quality was detected all around the globe after lockdowns enforced in several cities in India. The lockdown influenced the environment’s pollution level and improved air quality quickly due to very few human activities. The present work scientifically analyses the air pollutants (PM2.5, PM10, NO2, and SO2) with meteorological parameters in the golden quadrilateral cities. The purpose of this paper is to review the analysis of air quality of golden quadrilateral cities (Delhi, Kolkata, Chennai, and Mumbai). Data of air quality parameters are collectively taken from different locations from different regions of Delhi, Kolkata, Chennai, and Mumbai before lockdown and during the lockdown and compared the data of both periods. Comparison pre-lockdown and 2019 with respect to lockdown and 2020 respectively show a huge reduction in amounts of pollutants. Our objective is to find the implication of different lockdown measures on air quality levels in Delhi, Kolkata, Chennai, and Mumbai particularly this investigation is focused on PM2.5, PM10, NO2, SO2 which is directly transmitted by human action and formed through a chemical reaction in the atmosphere as well as quantify the short-range and long-range health impact.

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 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.

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