Publications

Formation of stratospheric nitric acid by a hydrated ion cluster reaction: Implications for the effect of energetic particle precipitation on the middle atmosphere.

Kvissel, O.-K.; Orsolini, Y.J.; Stordal, F.; Isaksen, I.S.A.; Santee, M.L.

Formation of secondary organic aerosol from isoprene oxidation over Europe.

Karl, M.; Tsigaridis, K.; Vignati, E.; Dentener, F.

Formation of secondary organic aerosol from isoprene oxidation over Europe.

Karl, M.; Tsigaridis, K.; Vignati, E.; Dentener, F.

Forest damage and forest monitoring in Norway - Annual report of the Norwegian Monitoring Programme for Forest Damage 2000. Rapport fra skogforskningen, 7/01

Aamlid, D.; Solberg, S.; Hylen, G, Tørseth, K.

Forest damage and forest monitoring in Norway - Annual report of Norwegian Monitoring Programme for Forest Damage 1998. Rapport fra skogforskningen, 11/99

Aamlid, D.; Solberg, S.; Hylen, G, Tørseth, K.

Forecasting the Exceedances of PM2.5 in an Urban Area

Logothetis, Stavros-Andreas; Kosmopoulos, Georgios; Panagopoulos, Orestis; Salamalikis, Vasileios; Kazantzidis, Andreas

Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air-quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, low-cost sensors have recently emerged as a valuable tool, facilitating the spatiotemporal monitoring of air pollution on a local scale. In this study, we present a deep learning approach (long short-term memory, LSTM) to forecast the intra-day air pollution exceedances across urban and suburban areas. The PM2.5 data used in this study were collected from 12 well-calibrated low-cost sensors (Purple Air) located in the greater area of the Municipality of Thermi in Thessaloniki, Greece. The LSTM-based methodology implements PM2.5 data as well as auxiliary data, meteorological variables from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF, and time variables related to local emissions to enhance the air pollution forecasting performance. The accuracy of the model forecasts reported adequate results, revealing a correlation coefficient between the measured PM2.5 and the LSTM forecast data ranging between 0.67 and 0.94 for all time horizons, with a decreasing trend as the time horizon increases. Regarding air pollution exceedances, the LSTM forecasting system can correctly capture more than 70.0% of the air pollution exceedance events in the study region. The latter findings highlight the model’s capabilities to correctly detect possible WHO threshold exceedances and provide valuable information regarding local air quality.

MDPI

Forecasting India's water future.

Mesquita, M. d. S.; Veldore, V.; Li, L.; Krishnan, R.; Orsolini, Y.; Senan, R.; Ramarao, M. V. S.; Viste, E.

Forecasted deep stratospheric intrusions over Central Europe: case studies and climatologies.

Trickl, T.; Feldmann, H.; Kanter, H.-J.; Scheel, H.-E.; Sprenger, M.; Stohl, A.; Wernli, H.

Forecast skill of autumn snow for European winter climate during the 20th century: A multi member seasonal prediction experiment

Wegmann, Martin; Orsolini, Yvan J.; Weisheimer, Antje; van den Hurk, Bart; Lohmann, Gerrit

Fordeling av PCB i snøprøver fra Nord-Norge. NILU F

Enge, E. K.; Heimstad, E. S.; Kallenborn, R.

Food and health security in the Norwegian, Finnish and Russia border region: linking local industries, communities and socio-economic impacts.

Sandanger, T.M.; Anda, E.E.; Rautio, A.; Dudarev, A.; Evenset, A.; Gwynn, J.; Mikkelsen, E.; Aleksandrovich, N.; Gjørv, G.H.; Kovalenko, A.; Christiansen, B.; Patero, J.

Follow-up & Conclusion of last IPR Technical Meeting & AQ e-reporting status

Soares, Joana; Targa, Jaume