scholarly journals Accounting for local meteorological effects in the ozone time-series of Lovozero (Kola Peninsula)

2003 ◽  
Vol 3 (1) ◽  
pp. 655-676 ◽  
Author(s):  
O. A. Tarasova ◽  
A. Y. Karpetchko
Keyword(s):  
Regression Model ◽  
Kola Peninsula ◽  
Seasonal Cycle ◽  
Regression Models ◽  
Meteorological Parameters ◽  
Surface Ozone ◽  
Meteorological Conditions ◽  
Short Term ◽  
The Impact ◽  
Meteorological Effects

Abstract. The impact of local meteorological conditions on surface ozone was studied by means of regression models creation. Ozone and meteorological parameters measured at Lovozero site (250 m a.s.l., 68.5° N, 35.0° E, Kola Peninsula) for the period of 1999–2000 were used. The regression model of daily mean ozone concentrations on the meteorological parameters like temperature, relative humidity, and wind speed can explain up to 70% of the ozone variability, if the seasonal cycle is also considered. A regression model was created for separated time scales of the variables. The separation of short-term, synoptical and seasonal components was done by means of Kolmogorov-Zurbenko filtering. The synoptical scale variations were chosen as the most informative from the point of their relation with meteorological parameters. About 40% of synoptical scale variations of surface ozone can be explained by regression model on separated meteo parameters that is 30% more efficient than ozone residuals usage.

scholarly journals Accounting for local meteorological effects in the ozone time-series of Lovozero (Kola Peninsula)

2003 ◽  
Vol 3 (4) ◽  
pp. 941-949 ◽  
Author(s):  
O. A. Tarasova ◽  
A. Yu. Karpetchko
Keyword(s):  
Regression Model ◽  
Kola Peninsula ◽  
Meteorological Parameters ◽  
Surface Ozone ◽  
Meteorological Condition ◽  
Cold Season ◽  
Meteorological Conditions ◽  
Short Term ◽  
The Relationship ◽  
Meteorological Effects

Abstract. The relationship between local meteorological conditions and the surface ozone variability was studied by means of statistical modeling, using ozone and meteorological parameters measured at Lovozero (250 m a.s.l., 68.5°N, 35.0°E, Kola Peninsula) for the period of 1999-2000. The regression model of daily mean ozone concentrations on such meteorological parameters as temperature, relative humidity and wind speed explains up to 70% of day-to-day ozone variability in terms of meteorological condition changes, if the seasonal cycle is also considered. A regression model was created for separated time scales of the variables. Short-term, synoptical and seasonal components are separated by means of Kolmogorov-Zurbenko filtering. The synoptical scale variations were chosen as the most informative from the point of their mutual relation with meteorological parameters. Almost 40% of surface ozone variations in time periods of 11-60 days can be explained by the regression model on separated scales that is 30% more efficient than ozone residuals usage. Quantitative and qualitative estimations of the relations between surface ozone and meteorological predictors let us preliminarily conclude that at the Lovozero site surface ozone variability is governed mainly by dynamical processes of various time scale rather than photochemistry, especially during the cold season.

scholarly journals Coronametrics: The UK turns the corner

2020 ◽  
Author(s):  
Adam Goliński ◽  
Peter Spencer
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Regression Models ◽  
List Type ◽  
Short Term ◽  
Time Series Regression ◽  
Current Trends ◽  
Short Term Forecasting ◽  
The Uk ◽  
The Impact

Abstract*There are many ways of analyzing the progress of an epidemic, but when it comes to short term forecasting, it is very hard to beat a simple time series regression model. These are good at allowing for the noise in day to day observations, extracting the trend and projecting it forward.*Our regression models are designed to exploit this, using the daily statistics released by PHE and NHSE. These strongly suggest that the tide has turned and that taking one day with the next, the national figures for deaths from this virus will now fall back noticeably, easing the pressure on the NHS and its staff.*There is still a huge range of uncertainty associated with any forecast. The model is currently predicting a total of 113,000 admissions to UK hospitals by the end of April and that 19,000 people will die from the virus in English hospitals by then. There is a 1 in 20 chance that the mortality figures could flatten out more quickly, with around 1,000 more deaths occurring by the end of April. However, there is the same risk that this figure continues to mount, rising to a total of 24,000 by the end of the month. On current trends, the number of deaths in the UK is likely to be 10% higher than the number in England.*Longer term, the impact of the virus will depend critically upon the likely relaxation of the current government strategy of suppression.

scholarly journals Sensitivity of Surface Ozone Simulation to Cumulus Parameterization

2008 ◽  
Vol 47 (5) ◽  
pp. 1456-1466 ◽  
Author(s):  
Zhining Tao ◽  
Allen Williams ◽  
Ho-Chun Huang ◽  
Michael Caughey ◽  
Xin-Zhong Liang
Keyword(s):  
Cloud Cover ◽  
Surface Ozone ◽  
Meteorological Conditions ◽  
Cumulus Parameterization ◽  
Daily Maximum ◽  
Boundary Layer Height ◽  
Ozone Concentrations ◽  
Simulated Precipitation ◽  
Cumulus Schemes ◽  
The Impact

Abstract Different cumulus schemes cause significant discrepancies in simulated precipitation, cloud cover, and temperature, which in turn lead to remarkable differences in simulated biogenic volatile organic compound (BVOC) emissions and surface ozone concentrations. As part of an effort to investigate the impact (and its uncertainty) of climate changes on U.S. air quality, this study evaluates the sensitivity of BVOC emissions and surface ozone concentrations to the Grell (GR) and Kain–Fritsch (KF) cumulus parameterizations. Overall, using the KF scheme yields less cloud cover, larger incident solar radiation, warmer surface temperature, and higher boundary layer height and hence generates more BVOC emissions than those using the GR scheme. As a result, the KF (versus GR) scheme produces more than 10 ppb of summer mean daily maximum 8-h ozone concentration over broad regions, resulting in a doubling of the number of high-ozone occurrences. The contributions of meteorological conditions versus BVOC emissions on regional ozone sensitivities to the choice of the cumulus scheme largely offset each other in the California and Texas regions, but the contrast in BVOC emissions dominates over that in the meteorological conditions for ozone differences in the Midwest and Northeast regions. The result demonstrates the necessity of considering the uncertainty of future ozone projections that are identified with alternative model physics configurations.

scholarly journals Working with Gaussian Random Noise for Multi-Sensor Archaeological Prospection: Fusion of Ground Penetrating Radar Depth Slices and Ground Spectral Signatures from 0.00 m to 0.60 m below Ground Surface

2019 ◽  
Vol 11 (16) ◽  
pp. 1895 ◽  
Author(s):  
Agapiou ◽  
Sarris
Keyword(s):  
Regression Model ◽  
Normal Distribution ◽  
Ground Penetrating Radar ◽  
Regression Models ◽  
Random Noise ◽  
Ground Surface ◽  
Archaeological Prospection ◽  
Spectral Signatures ◽  
Ground Penetrating ◽  
The Impact

The integration of different remote sensing datasets acquired from optical and radar sensors can improve the overall performance and detection rate for mapping sub-surface archaeological remains. However, data fusion remains a challenge for archaeological prospection studies, since remotely sensed sensors have different instrument principles, operating in different wavelengths. Recent studies have demonstrated that some fusion modelling can be achieved under ideal measurement conditions (e.g., simultaneously measurements in no hazy days) using advance regression models, like those of the nonlinear Bayesian Neural Networks. This paper aims to go a step further and investigate the impact of noise in regression models, between datasets obtained from ground-penetrating radar (GPR) and portable field spectroradiometers. Initially, the GPR measurements provided three depth slices of 20 cm thickness, starting from 0.00 m up to 0.60 m below the ground surface while ground spectral signatures acquired from the spectroradiometer were processed to calculate 13 multispectral and 53 hyperspectral indices. Then, various levels of Gaussian random noise ranging from 0.1 to 0.5 of a normal distribution, with mean 0 and variance 1, were added at both GPR and spectral signatures datasets. Afterward, Bayesian Neural Network regression fitting was applied between the radar (GPR) versus the optical (spectral signatures) datasets. Different regression model strategies were implemented and presented in the paper. The overall results show that fusion with a noise level of up to 0.2 of the normal distribution does not dramatically drop the regression model between the radar and optical datasets (compared to the non-noisy data). Finally, anomalies appearing as strong reflectors in the GPR measurements, continue to provide an obvious contrast even with noisy regression modelling.

Environmental Assessment of the Impact of Meteorological Parameters on Man-Made Pollution by Carcinogenicly Dangerous Chemicals in the Voronezh Air Basin

2021 ◽  
Vol 25 (2) ◽  
pp. 60-65
Author(s):  
S.A. Kurolap ◽  
V.S. Petrosyan ◽  
O.V. Klepikov ◽  
V.V. Kulnev ◽  
D.Yu. Martynov
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Meteorological Parameters ◽  
Carcinogenic Risk ◽  
Meteorological Conditions ◽  
Air Humidity ◽  
Maximum Permissible Level ◽  
Relative Air Humidity ◽  
The Impact ◽  
The City

Based on the analysis of official statistics from the Voronezh Hydrometeorological Service, the patterns of the dynamics of pollutants (formaldehyde and soot) are investigated depending on the combination of various meteorological parameters — air temperature, wind speed, relative air humidity. A positive relationship has been established between the increase in atmospheric pollution with formaldehyde and air temperature. With increasing wind speed and relative humidity, the concentration of formaldehyde and soot in the atmosphere of the city, as a rule, decrease. The maximum permissible level of carcinogenic risk to public health has been established, causing concern. The obtained patterns can be used to predict the level of technogenic pollution of the city’s atmosphere, depending on meteorological conditions.

scholarly journals The impact of weather and atmospheric circulation on O<sub>3</sub> and PM<sub>10</sub> levels at a rural mid-latitude site

2009 ◽  
Vol 9 (8) ◽  
pp. 2695-2714 ◽  
Author(s):  
M. Demuzere ◽  
R. M. Trigo ◽  
J. Vila-Guerau de Arellano ◽  
N. P. M. van Lipzig
Keyword(s):  
Air Quality ◽  
The Netherlands ◽  
Correlation Analysis ◽  
General Circulation ◽  
Surface Ozone ◽  
Weather Type ◽  
Climate Scenarios ◽  
Short Term ◽  
Air Quality Forecast ◽  
The Impact

Abstract. In spite of the strict EU regulations, concentrations of surface ozone and PM10 often exceed the pollution standards for the Netherlands and Europe. Their concentrations are controlled by (precursor) emissions, social and economic developments and a complex combination of meteorological actors. This study tackles the latter, and provides insight in the meteorological processes that play a role in O3 and PM10 levels in rural mid-latitudes sites in the Netherlands. The relations between meteorological actors and air quality are studied on a local scale based on observations from four rural sites and are determined by a comprehensive correlation analysis and a multiple regression (MLR) analysis in 2 modes, with and without air quality variables as predictors. Furthermore, the objective Lamb Weather Type approach is used to assess the influence of the large-scale circulation on air quality. Keeping in mind its future use in downscaling future climate scenarios for air quality purposes, special emphasis is given to an appropriate selection of the regressor variables readily available from operational meteorological forecasts or AOGCMs (Atmosphere-Ocean coupled General Circulation Models). The regression models perform satisfactory, especially for O3, with an (R2 of 57.0% and 25.0% for PM10. Including previous day air quality information increases significantly the models performance by 15% (O3) and 18% (PM10). The Lamb weather types show a seasonal distinct pattern for high (low) episodes of average O3 and PM10 concentrations, and these are clear related with the meteorology-air quality correlation analysis. Although using a circulation type approach can provide important additional physical relations forward, our analysis reveals the circulation method is limited in terms of short-term air quality forecast for both O3 and PM10 (R2 between 0.12 and 23%). In summary, it is concluded that the use of a regression model is more promising for short-term downscaling from climate scenarios than the use of a weather type classification approach.

scholarly journals Sensitivity analysis of the surface ozone and fine particulate matter to meteorological parameters in China

2020 ◽  
Vol 20 (21) ◽  
pp. 13455-13466
Author(s):  
Zhihao Shi ◽  
Lin Huang ◽  
Jingyi Li ◽  
Qi Ying ◽  
Hongliang Zhang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Meteorological Parameters ◽  
Surface Ozone ◽  
Fine Particulate Matter ◽  
Great Influence ◽  
Meteorological Conditions ◽  
Daily Maximum ◽  
Fine Particulate ◽  
Surface O3

Abstract. Meteorological conditions play important roles in the formation of ozone (O3) and fine particulate matter (PM2.5). China has been suffering from serious regional air pollution problems, characterized by high concentrations of surface O3 and PM2.5. In this study, the Community Multiscale Air Quality (CMAQ) model was used to quantify the sensitivity of surface O3 and PM2.5 to key meteorological parameters in different regions of China. Six meteorological parameters were perturbed to create different meteorological conditions, including temperature (T), wind speed (WS), absolute humidity (AH), planetary boundary layer height (PBLH), cloud liquid water content (CLW) and precipitation (PCP). Air quality simulations under the perturbed meteorological conditions were conducted in China in January and July of 2013. The changes in O3 and PM2.5 concentrations due to individual meteorological parameters were then quantified. T has a great influence on the daily maximum 8 h average O3 (O3-8 h) concentrations, which leads to O3-8 h increases by 1.7 in January in Chongqing and 1.1 ppb K−1 in July in Beijing. WS, AH, and PBLH have a smaller but notable influence on O3-8 h with maximum change rates of 0.3 ppb %−1, −0.15 ppb %−1, and 0.14 ppb %−1, respectively. T, WS, AH, and PBLH have important effects on PM2.5 formation of both in January and July. In general, PM2.5 sensitivities are negative to T, WS, and PBLH and positive to AH in most regions of China. The sensitivities in January are much larger than in July. PM2.5 sensitivity to T, WS, PBLH, and AH in January can be up to −5 µg m−3 K−1, −3 µg m−3 %−1, −1 µg m−3 %−1, and +0.6 µg m−3 %−1, respectively, and in July it can be up to −2 µg m−3 K−1, −0.4 µg m−3 %−1, −0.14 µg m−3 %−1, and +0.3 µg m−3 %−1, respectively. Other meteorological factors (CLW and PCP) have negligible effects on O3-8 h (less than 0.01 ppb %−1) and PM2.5 (less than 0.01 µg m−3 %−1). The results suggest that surface O3 and PM2.5 concentrations can change significantly due to changes in meteorological parameters, and it is necessary to consider these effects when developing emission control strategies in different regions of China.

scholarly journals The impact of corporate social responsibility on earnings quality in UK listed firms

2020 ◽  
Vol 9 (3) ◽  
pp. 678-695
Author(s):  
Zuhur Alatawi
Keyword(s):  
Regression Model ◽  
Financial Reporting ◽  
Regression Models ◽  
Earnings Quality ◽  
Listed Firms ◽  
Independent Variables ◽  
Size Growth ◽  
Corporate Social ◽  
The Impact ◽  
The Relationship

A business committed to CSR activities can establish a favourable reputation in the market hence this reputation can be used to mislead the market by making them rely on the financial reporting of the organisation. This study aimed to investigate the relationship between CSR and earnings quality for firms listed on FTSE 350. Besides, it aimed to explore the impact of CSR on the motivation of the management to improve the earnings quality or manage earnings. The research has applied LSDV regression and OLS regression on the data collected from 217 firms listed on the FTSE 350. The respective regression models applied by keeping earnings quality as a dependent variable and range of independent variables such as CSR, SIZE, GROWTH, LEVERAGE and ROA. Besides, the correlation coefficient has also been calculated despite, the result could not reveal the nature of the relationship between the variables hence regression model was applied. The results have revealed no relationship between earnings quality and CSR in the case of LSDV regression model. The same has been observed for the OLS model however, there exists a relatively significant relationship between earnings quality and LEVERAGE. Similar findings recorded for earnings quality and GROWTH.

scholarly journals The impact of weather and atmospheric circulation on O<sub>3</sub> and PM<sub>10</sub> levels at a mid-latitude site

2008 ◽  
Vol 8 (6) ◽  
pp. 21037-21088 ◽  
Author(s):  
M. Demuzere ◽  
R. M. Trigo ◽  
J. Vila-Guerau de Arellano ◽  
N. P. M. van Lipzig
Keyword(s):  
Air Quality ◽  
The Netherlands ◽  
Correlation Analysis ◽  
General Circulation ◽  
Surface Ozone ◽  
Weather Type ◽  
Climate Scenarios ◽  
Short Term ◽  
Air Quality Forecast ◽  
The Impact

Abstract. In spite of the strict EU regulations, concentrations of surface ozone and PM10 often exceed the pollution standards for The Netherlands and Europe. Their concentrations are controlled by (precursor) emissions, social and economic developments and a complex combination of meteorological actors. This study tackles the latter, and provides insight in the meteorological processes that play a role in O3 and PM10 levels in Cabauw (The Netherlands). The relations between meteorological actors and air quality are studied on a~local scale based on observations from Cabauw and are determined by a comprehensive correlation analysis and a multiple regression (MLR) analysis in 2 modes, with and without air quality variables as predictors. Furthermore, the objective Lamb Weather Type (WT) approach based on ECMWF (European Center for Medium-range Weather Forecasting) operational data is used to assess the influence of the large-scale circulation on air quality. Keeping in mind its future use in downscaling future climate scenarios for air quality purposes, special emphasis is given to an appropriate selection of the regressor variables readily available from operational meteorological forecasts or OAGCMs (Ocean-Atmosphere coupled General Circulation Models). The regression models perform satisfactory for both O3 and PM10, with an increased performance when including previous days air quality information. The lamb weather types show a seasonal distinct pattern for high (low) episodes of average O3 and PM10 concentrations, and these are clear related with the meteorology-air quality correlation analysis. Although using a circulation type approach can bring some interesting physical relations forward, our analysis reveals the circulation method is limited in terms of short-term air quality forecast for both O3 and PM10. In summary, it is concluded that the use of a regression model is more promising for short-term downscaling from climate scenarios than the use of a weather type classification approach.

Investigating the Impacts of Meteorological Parameters on Air Pollution in Shanghai

2014 ◽  
Vol 675-677 ◽  
pp. 643-646 ◽  
Author(s):  
Dan Xue ◽  
Qian Liu
Keyword(s):  
Air Pollution ◽  
Regression Model ◽  
Regression Models ◽  
Meteorological Parameters ◽  
Multiple Regression Model ◽  
Air Pollutant ◽  
Urban Atmosphere ◽  
Linear Regression Models ◽  
Pollution Levels ◽  
Pollution Concentrations

Air quality has been deteriorated seriously in Shanghai as a result of urbanization and modernization. Air pollution concentrations were decreased during the period of 2008-2011. PM10, SO2and NO2concentrations were higher in winter than in summer. Meteorological conditions affect air pollution levels in the urban atmosphere significantly due to their important role in transport and dilution of the pollutants. Multiple linear regression models were used to predict next day’s PM10, SO2and NO2concentrations, respectively. The calculatedR2values were 0.753, 0.800 and 0.861 for PM10, SO2and NO2regression model, respectively. This result shows the multiple regression model analysis, providing simple and faster application facilities, is useful for modeling the impacts of meteorological factors on air pollutant levels.

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