Wyniki wyszukiwania - Biblioteka Nauki

1

Urban Air Quality Monitoring and Presentation of Results in the Electronic Media in Prishtina, Kosovo

100%

Hajrizi Z. , Balaj N. , Rizani H.

Ecological Engineering & Environmental Technology

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2023

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tom Vol. 24, iss. 5

11--19

EN

Air pollution is a global threat leading to large impacts on health and urban ecosystems.The air quality index is based on measurement of particulate matter (PM2.5 and PM10), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Carbon Monoxide (CO) and Ozone (O3). Recent the years in Kosovo are installed nation air quality monitoring in different areas such as residential areas, industrial, roadside and reference areas. The study was conducted in Prishtina region between November-December 2021 and January 2022 in five monitoring stationes. The purpose of this paper is to determine compliance with air quality limit value, to detect pollutant levels (NO2, SO2, CO, O3), particulate matter (PM10 and PM2.5) and to study the values of exceedances, from the standards values for air quality. Air quality monitoring in this study was done in the study area Agglomeration-AKS1 (IHMK, ex-Rilindja, Obiliq, Dardhishte and Palaj). Particulate matter (PM10) and (PM2.5) and Nitrogen Dioxide (NO2) have shown exceedancces value from the standard values for air quality in Agglomeration-AKS1area in Prishtina. Nitrogen Dioxide (NO2) have shown exceedancces value (100-120 μg/m3) from the standard values for air quality at the ex-Rilindja (132.2 μg/m3). PM2.5 has exceeded the limit values (20–25 μg/m3) in monitoring stations:IHMK,Obiliq, Dardhishte and Palaj. The respect and application of international standards for air quality strengthens the image of Kosovo, preserves the health of citizens, fulfills environmental criteria, while the contribution of the media is considered important.

2

Optimal Prediction of Air Quality Index in Metropolitan Cities Using Fuzzy Time Series with Deep Learning Approach

100%

Unnikrishnan A. , Rajeswari S.

Ecological Engineering & Environmental Technology

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2024

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tom Vol. 25, iss. 1

341--359

EN

Predicting the air quality index (AQI) with high accuracy is just as crucial as predicting the weather. The research selected a few potential meteorological parameters and historical data after taking into account a variety of complex factors to accurately anticipate AQI. The dataset was gathered, pre-processed to substitute missing values (MV) and eliminate redundant information, and before being applied to predict the AQI. The data was collected from 2019 to 2022 to analyse the AQI founded on time series forecasting (TSF). Many AQI parameters, including accumulated precipitation, the daily normal temperature, and prevailing winds, are lacking in this study. To preserve the characteristics of the time series, kNN classification was implemented to fill in the MV and integrate Principal Component Analysis (PCA) to decrease the noise of data to recover the accuracy of AQI prediction. However, the majority of research is limited due to a lack of panel data, which means that characteristics such as seasonal behaviour cannot be taken into account. Consequently, the research introduced a TSF based on seasonal autoregressive integrated moving average (SARIMA) and stochastic fuzzy time series (SFTS). The stacked dilated convolution technique (SDCT) which effectively extracts the time autocorrelation, while the time attention module focuses on the time intervals that were significantly linked with each instant. To control the strongly connected features in the data set, the Spearman rank correlation coefficient (SRCC) was utilised. The selected features included SO2, CO and O3, NO2, PM10 and PM2.5, temperature, pressure, humidity, wind speed and weather, as well as rainfall. Additionally, to estimate the AQI and SO2, PM10, PM2.5, NO2, CO, and O3 concentration from 2019 to 2022, the data of climatological elements after PCA and historical AQI were input into the multiple linear regression (MLR) techniques with a temporal convolution network (TCN) built deep learning model (DLM). The proposed DLM springs a correct and detailed assessment for AQI prediction. The experimental results confirm that the expected background yields a stable forecasting result, that the pollutant concentration of the surrounding areas affects the AQI of a place, and that the planned model outperforms existing state-of-the-art models in terms of prediction of consequences. Consequently, utilising this presented innovative approach integrates fuzzy time series with deep learning, addressing missing values and noise reduction, incorporating seasonal behaviour, utilising the SRCC for feature control, employing a comprehensive set of meteorological parameters, and presenting a hybrid model that outperforms existing models. These aspects collectively contribute to the advancement of air quality prediction methodologies, particularly in metropolitan cities. However, this hybrid approach leverages the strengths of both traditional statistical methods and deep learning techniques, resulting in a robust and accurate assessment for AQI prediction as well as providing more stable and accurate forecasting results.

3

Patterns of relationship between PM10 from air monitoring quality station and AOT data from MODIS sensor onboard of Terra satellite

86%

Suriya W. , Chunpang P. , Laosuwan T.

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tom Vol. 30, No. 2

236--249

EN

Thailand, especially in the northern region, often encounters the problem of having PM10 exceeding the normal standard level, which could do harm to people’s health. Mostly, such problem is caused by the burning of forest area and open area; this is clearly seen during January–April of every year. Also, the problem as mentioned is caused by the meteorological conditions and the terrains in the northern region that make it easy for PM10 to be accumulated. The aim of this study was to analyze the patterns of relationship between PM10 measured from the ground monitoring station and AOT data received from MODIS sensor onboard of Terra satellite in Phrae Province located in the northern region of Thailand. The method performed was by analyzing the correlation between PM10 data obtained from the ground monitoring station and the AOT data received from the MODIS sensor onboard of Terra satellite during January–April 2018. It was found from the study that the change of the intensity of PM10 and AOT in the climate was highly related; it appeared that the correlation coefficient (r) in January–April was 0.92, 0.91, 0.91 and 0.92, respectively. This research pointed out that during February– –April, the areas of Phrae Province had the level of PM10 that affected health. Besides, from the method in this research, it revealed AOT data received from MODIS sensor onboard of Terra satellite could be applied in order to follow up, monitor, and notify the spatial changes of PM10 efficiently.

4

Ocena zmian zanieczyszczenia powietrza w Polsce w latach 1997-2009 z wykorzystaniem indeksu jakości powietrza

86%

Kobus D.

Przemysł Chemiczny

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2011

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tom T. 90, nr 2

200-207

5

Patterns of relationship between PM10 from air monitoring quality station and AOT data from MODIS sensor onboard of Terra satellite

86%

Suriya W. , Chunpang P. , Laosuwan T.

Przegląd Naukowy. Inżynieria i Kształtowanie Środowiska

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2021

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tom 30

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nr 2[92]

6

Air quality index and its significance in environmental health risk communication

86%

Kowalska M. , Ośródka L. , Klejnowski K. , Zejda J. , Krajny E. , Wojtylak M.

Archives of Environmental Protection

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2009

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tom Vol. 35, no. 1

13-21

EN

Air Quality Index (AQ1) is a standardized summary measure of ambient air quality used to express the level of health risk related to particulate and gaseous air pollution. The index, first introduced by US EPA in 1998 classified ambient air quality according to concentrations of such principal air pollutants as PMIO, PM,5, ozone, SO_2 NO, and CO. Subsequently similar, index-based approach to express health risk was developed in France, Great Britain and Germany. No such environmental warning system exists in Poland, although some test-trials took place in Katowice area and the city of Gdańsk. However, the operational value of AQ1 under environmental circumstances in Poland remains unknown. The aim of the study was to examine current air pollution levels in Katowice area and to confront AQ1 categories with local air quality, also in terms of health impact on the population as expressed by daily total and specific mortality. The data on daily average PM|(| and sulphur dioxide concentrations available in regional network (P1OŚ in Katowice) and data on daily number of total deaths and deaths due to cardiorespiratory diseases from the Central Statistical Office in Warsaw were collected. The data covered the period 2001-2002. The percentage of days with individual Air Quality Index, created by American, French, British and German method of indexation was calculated. Then, the relationship between values of air quality indexes and daily total and specific mortality according to Spearman correlation coefficients was assessed. Finally, the obtained results were verified according to ANOVA Kruskal-Wallis test. The obtained results suggest significant discrepancy in the range of air quality categories depending on applied system of classification. Percentage of days with "unhealthy" air quality (in the period 2001-2002) was running from 0.1% (American method of indexation) to 11.2% (British method) and usually referred to winter season. Statistically significant Spearman correlation coefficients were obtained for the relationship between air quality and total number of deaths, as well as the number of deaths due to cardiovascular and respiratory diseases in elderly population (aged 65 and more). The observed values of correlation coefficients arc very low and do not exceed value 0.2 for each chosen method of indexation.

PL

Indeks jakości powietrza (AQ1) jest wskaźnikiem określającym jakość powietrza atmosferycznego i jednocześnie wskazującym potencjalne ryzyko zdrowotne ponoszone przez populację wskutek narażenia na standardowo mierzone stężenia zanieczyszczeń pyłowych i gazowych w danym regionie. Po raz pierwszy został użyty przez US EPA w 1998 r. i klasyfikował jakość powietrza atmosferycznego w oparciu o stężenia podstawowych zanieczyszczeń: PMln, PM,5, ozonu, SO,, NO, oraz CO. Podobne wskaźniki, oparte na danych regionalnych opracowano również we Francji, Wielkiej Brytanii i Niemczech. Właściwie w naszym kraju nie funkcjonuje spójny system komunikowania ryzyka zdrowotnego, który byłby oparty na własnym indeksie jakości powietrza, chociaż pewne próby podejmowane są w Katowicach i Gdańsku. Celem prezentowanej pracy była ocena jakości powietrza atmosferycznego w Katowicach na podstawie przyjętych kategorii AQ1 oraz porównanie uzyskanych danych z danymi opisującymi potencjalne ryzyko zdrowotnego wyrażone w postaci dobowej umieralności całkowitej lub specyficznej. Zebrano dane dotyczące średnich dobowych stężeń pyłu PM10 oraz dwutlenku siarki dostępne w ramach regionalnego monitoringu środowiska (P1OŚ w Kato-wicach) oraz dane dotyczące dobowej liczby zgonów ogółem i zgonów z powodu chorób układu oddechowego i krążenia pochodzące z bazy Głównego Urzędu Statystycznego w Warszawie. Wszystkie dane dotyczyły okresu 2001-2002. Obliczono odsetki dni z właściwym dla nich indeksem jakości powietrza stosując amerykański, francuski, brytyjski i niemiecki sposób indeksowania. Następnie oceniono zależność pomiędzy przyjętą kategorią jakości powietrza a dobową umieralnością ogólną i specyficzną z zastosowaniem współczynników korelacji Spearmana. Ostatecznie uzyskane wyniki zweryfikowano przy użyciu testu ANOVA Kruskal-Wallis. Uzyskane wyniki sugerują występowanie istotnego zróżnicowania w zakresie kategorii jakości powietrza atmosferycznego, zależnie od przyjętego sposobu klasyfikacji. Procent dni z tzw. "niezdrową" jakością powietrza kształtował się w badanym okresie (2001-2002) w zakresie od 0,1% (amerykański sposób indeksowania) do 11,2% (brytyjski sposób indeksowania) i zazwyczaj kategoria dotyczyła okresu zimy. Statystycznie znamienne wartości współczynników korelacji Spearmana uzyskano jedynie dla zależności pomiędzy jakością powietrza a dobową liczbą zgonów ogółem oraz zgonów z powodu chorób układu oddechowego i krążenia w grupie osób po 65 roku życia. Jednakże zaobserwowane wartości współczynników były niewielkie i nie przekraczały wartości 0,2 dla każdej z przyjętych metod klasyfikacji.

7

Indeks jakości powietrza

72%

Bielawska M. , Wardencki W.

Analityka : nauka i praktyka

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2014

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tom nr 4

60--64

PL

Indeks jakości powietrza to doskonałe narzędzie do przekazywania informacji na temat poziomu zanieczyszczeń opinii publicznej za pośrednictwem mediów lub coraz bardziej dostępnego internetu.

8

Wskaznik Wjp jako miernik zanieczyszczenia powietrza atmosferycznego

58%

Olejnik M.

Acta Scientiarum Polonorum. Formatio Circumiectus

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2005

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tom 04

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nr 1

47-58

9

COVID-19: A spectio-temporal analysis of air quality status in coking coalfields of India due to nationwide lockdown in India

58%

Singh A. , Singh G.

Journal of Sustainable Mining

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2020

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tom Vol. 19, iss. 4

243--253

EN

A novel infectious corona virus disease (COVID-19) was identified in the month of December 2019. It has now been announced as a worldwide pandemic by the World Health Organization. COVID-19 pandemic has positive impacts on the environmental pollutants. In present work, Coalfield areas of Jharia Coalfields (JCF), India have been taken as a case study to evaluate the effect of the lockdown on air quality at 10 locations. This study had been selected to estimate the reduction in concentration of pollutants likePM10, PM2.5, SO2, and NOx during 3 Seasons (summer, Post-Monsoon and winter season) in the year 2019 in comparison to the concentration during the lockdown period i.e. from April 2020 to June 2020. The study areas selected was as fire affected and non-fire affected areas of Jharia Coalfield to identify the contribution of pollutants in the mining area to establish the baseline concentration of Business as usual (BAU) vs. the lockdown condition. The average reduction in concentration of PM10, PM2.5, SO2 and NOx was observed as 18%, 14%, 22% and 26% respectively during the lockdown period in comparison with the annual average concentration. As observed, the AQI value at the selected monitoring sites in JCF was 1.5 times higher in comparison to the lockdown period. This study will provide the confidence to the regulatory body for strict implementation of the applicable air quality standard/policies in the mining areas. The study will also provide confidence to the regulatory body in making emission control strategies for improvement of environmental conditions and human health.