Analysis of trends in annual time series of precipitation in the Republic of Bashkortostan, Russian Federation

Gaponov, V M; Elizaryev, Alexey; Aksenov, Sergey; Longobardi, Antonia

doi:10.1088/1755-1315/350/1/012003

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…These results suggest that the concentrations, in μg m −3 , of NO and NO 2 decreased during the period of Jan 14th-April 12th at the monthly rate presented by the third columns of Tables 2 and 3. Moreover, columns "P1" and "P2" (Tables 2 and 3) show the intervention points determined by the Pettitt test (Salarijazi et al 2012;Ferreira et al 2015;Jaiswal et al 2015;Ahmad et al 2018;Penereiro and Meschiatti 2018;Gaponov et al 2019) for each period. In order to visualize the decreasing trends (negative values of Sen) and the intervention points, we have constructed Fig.…”

Section: Resultsmentioning

confidence: 99%

São Paulo’s atmospheric pollution reduction and its social isolation effect, Brazil

Rosse

Pereira

Boari

et al. 2020

Air Qual Atmos Health

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Since January 2020, studies report reductions in air pollution among several countries due to social isolation measures, which have been adopted in order to contain the coronavirus outbreak progress (COVID-19). This study aims to evaluate the change in the atmospheric pollution levels by NO and NO 2 in São Paulo City for the social isolation period. The NO and NO 2 hourly concentrations were obtained through air quality monitoring stations from CETESB, from January 14, 2020 to April 12, 2020. Mann-Kendall and the Pettitt tests were performed in the air pollutant time series. We observed an overall negative trend in all stations, indicating a decreasing temporal pattern in concentrations. Regarding NO, the highest absolute decrease rates were observed in the Congonhas (− 6.39 μg m −3 month −1 ) and Marginal Tietê (− 6.19 μg m −3 month −1 ) stations; regarding NO 2 , the highest rates were observed in the Marginal Tietê (− 4.45 μg m −3 month −1 ) and Cerqueira César (− 4.34 μg m −3 month −1 ) stations. In addition, we identified a turning point in the NO and NO 2 series trends that occurred close to the start date of the social isolation period (March 20, 2020). Moreover, from statistical analysis, it was found that NO 2 is a suitable surrogate for monitoring economic activities during social isolation periods. Thus, we concluded that social isolation measures implemented on March 20, 2020 caused significant changes in the air pollutant concentrations in the city of São Paulo (as high as − 200% in NO 2 levels).

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Section: Resultsmentioning

confidence: 99%

São Paulo’s atmospheric pollution reduction and its social isolation effect, Brazil

Rosse

Pereira

Boari

et al. 2020

Air Qual Atmos Health

View full text Add to dashboard Cite

show abstract

“…Moreover, columns "P1" and "P2" show the intervention points determined by the Pettitt test (Salarizaji et al 2012;Ferreira et al 2015;Jaiswal et al 2015;Ahmad et al 2018;Penereiro and Meschiatti 2018;Gaponov et al 2019) for each period. In order to visualize the decreasing trends and the intervention points, we have elaborated the Figure 2.…”

Section: Resultsmentioning

confidence: 99%

Atmospheric Pollution Reduction in São Paulo City and the Social Isolation Effect

Rosse¹,

Pereira²,

Boari³

et al. 2020

Preprint

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Since January 2020, some studies have been reporting a reduction in air pollution in several countries due to social isolation measures, which have been adopted in order to contain the coronavirus outbreak progress (Covid-19). This study aims to evaluate the change in the atmospheric pollution levels by NO and NO2 in São Paulo city in the social isolation period. The NO and NO2 hourly concentrations were obtained through air quality monitoring stations from CETESB, from January 14 to April 12, 2020. Mann-Kendall and the Pettitt tests were performed in the air pollutants time series. We observed an overall negative trend in all stations, indicating a decreasing temporal pattern in concentrations. Regarding NO, the highest decrease rates were observed in Congonhas (-6.39 µg.m-3month-1) and Marginal Tietê (-6.19 µg.m-3month-1) stations; regarding NO2, the highest rates were observed in Marginal Tietê (-4.45 µg.m-3month-1) and Cerqueira César (-4.34 µg.m-3month-1) stations. In addition, we identified a turning point in the NO and NO2 series trends that occurred close to the start date of the social isolation period (March 20, 2020). Moreover, from statistical analysis, it was found that NO2 is a suitable surrogate for monitoring economic activities during social isolation period. Thus, we concluded that social isolation measures implemented in March 20, 2020 caused significant changes in the air pollutants concentrations in São Paulo city (as high as -200% in NO2 levels).

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“…Mitigation and adaptation to the impact of climate change have gained unprecedented momentum in recent years (IPCC, 2018;Saint-Lu, et al, 2019). Especially after the 1990s, trend analysis has become a preferred topic among researchers and there are many trend methodology and application studies in the literature recently (Mohorji et al, 2017;Bera, 2017;Preethi et al, 2017;Bisht et al, 2018;Choudhury et al, 2019;Calojero, 2020;Fukushima et al, 2019;Gedefew et al, 2018;Gaponov et al, 2019;Gebrechorkos et al, 2019;Elzopy et al, 2020;Portela et al, 2020;Praveen et al, 2020;Wang et al, 2020;Saini and Sahu, 2021;Skea et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

Şen

2022

Preprint

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To meet the basic assumption of classical Mann-Kendall (MK) trend analysis, which requires serially independent time series, a pre-whitening (PW) procedure is proposed to alleviate the serial correlation structure of a given hydro-meteorological time series records for application. The procedure is simply to take the lagged differences in a given time series in the hope that the new time series will have an independent serial correlation coefficient. The whole idea was originally based on the first-order autoregressive AR (1) process, but such a procedure has been documented to damage the trend component in the original time series. On the other hand, the over-whitening procedure (OW) proposes a white noise process superposition of the same length with zero mean and some standard deviation on the original time series to convert it into serially independent series without any damage to the trend component. The stationary white noise addition does not have any trend components. For trend identification, annual average temperature records in New Jersey and Istanbul are presented to show the difference between PW and OW procedures. It turned out that the OW procedure was superior to the PW procedure, which did not cause a loss in the original trend component.

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Analysis of trends in annual time series of precipitation in the Republic of Bashkortostan, Russian Federation

Cited by 9 publications

References 15 publications

São Paulo’s atmospheric pollution reduction and its social isolation effect, Brazil

São Paulo’s atmospheric pollution reduction and its social isolation effect, Brazil

Atmospheric Pollution Reduction in São Paulo City and the Social Isolation Effect

Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

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