Fant 9758 publikasjoner. Viser side 370 av 391:
2017
Assessment of spatial and temporal variation in the impacts of ozone on human health, vegetation, and climate requires appropriate metrics. A key component of the Tropospheric Ozone Assessment Report (TOAR) is the consistent calculation of these metrics at thousands of monitoring sites globally. Investigating temporal trends in these metrics required that the same statistical methods be applied across these ozone monitoring sites. The nonparametric Mann-Kendall test (for significant trends) and the Theil-Sen estimator (for estimating the magnitude of trend) were selected to provide robust methods across all sites. This paper provides the scientific underpinnings necessary to better understand the implications of and rationale for selecting a specific TOAR metric for assessing spatial and temporal variation in ozone for a particular impact. The rationale and underlying research evidence that influence the derivation of specific metrics are given. The form of 25 metrics (4 for model-measurement comparison, 5 for characterization of ozone in the free troposphere, 11 for human health impacts, and 5 for vegetation impacts) are described. Finally, this study categorizes health and vegetation exposure metrics based on the extent to which they are determined only by the highest hourly ozone levels, or by a wider range of values. The magnitude of the metrics is influenced by both the distribution of hourly average ozone concentrations at a site location, and the extent to which a particular metric is determined by relatively low, moderate, and high hourly ozone levels. Hence, for the same ozone time series, changes in the distribution of ozone concentrations can result in different changes in the magnitude and direction of trends for different metrics. Thus, dissimilar conclusions about the effect of changes in the drivers of ozone variability (e.g., precursor emissions) on health and vegetation exposure can result from the selection of different metrics.
2018
This study quantifies the present-day global and regional distributions (2010–2014) and trends (2000–2014) for five ozone metrics relevant for short-term and long-term human exposure. These metrics, calculated by the Tropospheric Ozone Assessment Report, are: 4th highest daily maximum 8-hour ozone (4MDA8); number of days with MDA8 > 70 ppb (NDGT70), SOMO35 (annual Sum of Ozone Means Over 35 ppb) and two seasonally averaged metrics (3MMDA1; AVGMDA8). These metrics were explored at ozone monitoring sites worldwide, which were classified as urban or non-urban based on population and nighttime lights data.
Present-day distributions of 4MDA8 and NDGT70, determined predominantly by peak values, are similar with highest levels in western North America, southern Europe and East Asia. For the other three metrics, distributions are similar with North–South gradients more prominent across Europe and Japan. Between 2000 and 2014, significant negative trends in 4MDA8 and NDGT70 occur at most US and some European sites. In contrast, significant positive trends are found at many sites in South Korea and Hong Kong, with mixed trends across Japan. The other three metrics have similar, negative trends for many non-urban North American and some European and Japanese sites, and positive trends across much of East Asia. Globally, metrics at many sites exhibit non-significant trends. At 59% of all sites there is a common direction and significance in the trend across all five metrics, whilst 4MDA8 and NDGT70 have a common trend at ~80% of all sites. Sensitivity analysis shows AVGMDA8 trends differ with averaging period (warm season or annual). Trends are unchanged at many sites when a 1995–2014 period is used; although fewer sites exhibit non-significant trends. Over the longer period 1970–2014, most Japanese sites exhibit positive 4MDA8/SOMO35 trends. Insufficient data exist to characterize ozone trends for the rest of Asia and other world regions.
2018
2012
2013
2013
2006
Tunneler E134 Kongsberg. Vurdering av luftforurensning fra tunnelmunninger. NILU OR
Spredningsberegninger for tunnelforbindelser langs E134, Kongsberg. Det er beregnet maksimale konsentrasjoner av PM10,og NOX i tunnelene ved ugunstige trafikkforhold (rushtrafikk morgen/ettermiddag). Konsentrasjonsreduksjon som funksjon av avstand fra tunnelmunninger er vist i tabell, og konsentrasjonene er sammenlignet med Nasjonalt mål og grenseverdier for luftkvalitet
2011
Tunneler i Oslo. Luftkvalitetsberegning i forhold til Forurensningsloven. NILU OR
Forurensning rundt munningene av fire tunneler er beregnet, og situasjonen ved fem andre tunneler er vurdert i forhold til beregningsresultatene. Beregningene viser at ved tilsammen 6 av munningene kan forurensningskonsentrasjon over Forurensningslovens kartleggingsgrense forekomme ved bygninger. Ved en munning forekommer konsentrasjon over tiltaksgrensen ved bygning.
2001
Twelfth EIONET workshop on air quality management and assessment, Limassol, Cyprus 15-16 October 2007. Proceedings. ETC/ACC Technical paper, 2007/10
2007
2016
2003
2024
2016
2017
2004
Two-Stage Feature Engineering to Predict Air Pollutants in Urban Areas
Air pollution is a global challenge to human health and the ecological environment. Identifying the relationship among pollutants, their fundamental sources and detrimental effects on health and mental well-being is critical in order to implement appropriate countermeasures. The way forward to address this issue and assess air quality is through accurate air pollution prediction. Such prediction can subsequently assist governing bodies in making prompt, evidence-based decisions and prevent further harm to our urban environment, public health, and climate, all of which co-benefit our economy. In this study, the main objective is to explore the strength of features and proposed a two stage feature engineering approach, which fuses the advantage of influential factors along with the decomposition approach and generates an optimum feature combination for five major pollutants including Nitrogen Dioxide (NO 2 ), Ozone (O 3 ), Sulphur Dioxide (SO 2 ), and Particulate Matter (PM2.5, and PM10). The experiments are conducted using a dataset from 2015 to 2020 which is publicly available and is collected from Belfast-based air quality monitoring stations in Northern Ireland, UK. In stage-1, using the dataset new features such as trigonometric and statistical features are created to capture their dependency on the target pollutant and generated correlation-inspired best feature combinations to improve forecasting model performance. This is further enhanced in stage-2 by an optimum feature combination which is an integration of stage-1 and Variational Mode Decomposition (VMD) based features. This study employed a simplified Long Short Term Memory (LSTM) neural network and proposed a single-step forecasting model to predict multivariate time series data. Three performance indicators are used to evaluate the effectiveness of forecasting model: (a) root mean square error (RMSE), (b) mean absolute error (MAE), and (c) R-squared (R 2 ). The results demonstrate the effectiveness of proposed approach with 13% improvement in performance (in terms of R 2 ) and the lowest error scores for both RMSE and MAE.
IEEE (Institute of Electrical and Electronics Engineers)
2024