Publikasjoner

Tropospheric ozone research. Overview of subproject TOR-2.

Lindskog, A.; Beekmann, M.; Monks, P.; Roemer, M.; Schuepbach, E.; Solberg, S.

Tsetlin Machine Embedding: Representing Words Using Logical Expressions

Bhattarai, Bimal; Granmo, Ole-Christoffer; Jiao, Lei; Yadav, Rohan Kumar; Sharma, Jivitesh

Tungmetallutslipp til luft fra aluminiumsindustrien i Norge. NILU OR

Tønnesen, D.; Haugsbakk, I.

Tunneler E134 Kongsberg. Vurdering av luftforurensning fra tunnelmunninger. NILU OR

Haugsbakk, I.

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

Turbulent dispersion observed during the COMTESSA artificial release experiments

Dinger, Anna Solvejg; Stebel, Kerstin; Cassiani, Massimo; Ardeshiri, Hamidreza; Bernardo, Cirilo; Kylling, Arve; Park, Soon-Young; Pisso, Ignacio; Schmidbauer, Norbert; Stohl, Andreas

Twelfth EIONET workshop on air quality management and assessment, Limassol, Cyprus 15-16 October 2007. Proceedings. ETC/ACC Technical paper, 2007/10

Larssen, S.; de Leeuw, F.

Twilight tropospheric and stratospheric photodissociation rates derived from balloon borne radiation measurements.

Kylling, A.; Danielsen, T.; Blumthaler, M.; Schreder, J.; Johnsen, B.

Two mechanisms of stratospheric ozone loss in the Northern Hemisphere, studied using data assimilation of Odin/SMR atmospheric observations.

Sagi, K.; Pérot, K.; Muragh, D.; Orsolini, Y.

Two stage inversion method for microplastics emission estimation

Tichý, Ondřej; Evangeliou, Nikolaos; Šmídl, Václav

Two-Stage Feature Engineering to Predict Air Pollutants in Urban Areas

Naz, Fareena; Fahim, Muhammad; Cheema, Adnan Ahmad; Nguyen, Trung Viet; Cao, Tuan-Vu; Hunter, Ruth; Duong, Trung Q.

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)

Typical weather years and the effect of urban microclimate on the energy behaviour of buildings and HVAC systems.

Oxizidis, S.; Dudek, A.V.; Aquilina, N.

UFTIR Kick-off meeting – Nadir database and project website. NILU F

Vik, A.F.; Braathen, G.O.