Fant 9857 publikasjoner. Viser side 375 av 395:
Air Quality Impact Assessment, Kinyerezi, Dar Es Salaam, Tanzania. Phase II process update. NILU OR
Denne vurderingen av utslipp fra et planlagt forbedringsprosjekt for et kraftverk i Kinyerezi (Tanzania) er en oppfølging av vurdering i fase I, utført av NILU i 2012 (150 MW dual-fuel anlegg). Denne vurdering for fase II gjelder et naturgassdrevet kraftverk med totalt 8 generatorer og 345 MW. Konsentrasjonsutdelinger fra det planlagte kraftverket ble generert med TAPM (The Air Pollution Model), som brukte 2009 som studieperiode for å være sammenlignbar med vurderingen i fase I. Årlige CO2-utslipp er beregnet til 1,5 millioner tonn/år, som er 0,2% av de totale utslippene for hele landet i Tanzania. NOx -utslipp har størst innvirkning på lokalt nivå, med maksimumskonsentrasjoner (en-times) fra 10,7 til 33,2 ¿g/m3. Den gjennomsnittlige årlige konsentrasjonen er lav (høyeste er 1,4 µg/m3). Det planlagte kraftverket på Kinyerezi (alene) overstiger ikke lokale grenseverdier for luftkvalitet eller WHO retningslinjer.
2014
2003
Air quality forecasting and information towards public demonstrated in Wuhan, Hubei province, China. NILU F
2014
Air quality forecasting and information towards public demonstrated in Wuhan, Hubei province, China.
2014
This report analyses evolution and trends of air quality in Europe, based on a 15-year time series of spatial data fusion maps for the years 2005-2019. The analysis has been performed for PM10 annual average, the ozone indicator SOMO35 and NO2 annual average. For the purpose of the Eionet Report - ETC/ATNI 2021/11 trend analysis, a consistent reconstruction of the full 15-year time series of air quality maps has been performed, based on a consistent mapping methodology and input data. For the reconstruction, the Regression – Interpolation – Merging Mapping (RIMM) methodology as routinely used in the regular European-wide annual mapping has been applied.
The trend analysis has been performed based on time series of the aggregated data for individual countries, for large European regions and for the entire mapping area, both for spatial and population-weighted aggregations. In addition, maps of trends have been constructed based on the trend estimates for all grid cells of a map.
For the European-wide aggregations across the whole mapping area, statistically significant downward trend have been estimated for PM10 and NO2, while no significant trend was detected in the case of ozone.
ETC/ATNI
2021
Air quality data. Bakka station 2011-2012. NILU OR
Monitoring of ozone, nitrogen oxides and particulate matter (PM) have been performed at Bakka south of Mongstad for 13 consecutive full months (December 2011 to December 2012 inclusive). The nitrogen oxide and PM concentrations are very low. The ozone is at typical background ozone level.
2013
On behalf of Aluminiumindustriens Miljøsekretariat (AMS), NILU – Norwegian Institute for Air Research has conducted a sampling campaign in the surroundings of the Hydro Sunndal aluminium smelter in order to update the knowledge on air quality around the smelter today. Samples were taken in summer 2019 and analysed for PM2.5, PM10, metals (Pb, Cd, Cr, Ni, As, Al, V, Ga, Sb, Bi), particle-bound PAHs, SO2, particle-bound and gaseous fluorides. As a consequence of reduced emissions compared to earlier measurements, the ambient concentrations of PM10, Cr, Pb, BaP (for PAHs), SO2 and fluorides were strongly reduced. All measured compounds had concentrations below limit values and recommended
guideline values.
NILU
2021
On behalf of Aluminiumindustriens Miljøsekretariat (AMS), NILU – Norwegian Institute for Air Research reviewed existing data on ambient air quality around aluminium smelters from the period 1992 – 2020. Changes in production technologies and treatment technologies have been implemented in this time period. Emissions to air and ambient concentrations of most compounds typically measured (PAHs, fluorides, sulphur dioxide, particulate matter, heavy metals) have decreased since the beginning of the 1990s as a result of improvement of the production technology.
NILU
2022
2010
Air quality assessment and management in Europe and Norway: History, current status and future plans
2019
Monitoring air quality in ports and nearby cities is crucial to understanding the role of emissions from shipping and other port activities. This report analyzes air quality in 23 European ports, revealing limited observations in and around port areas. Only 5 of the 23 ports had at least one air quality sampling point for NO2 and PM10 inside the port area. Concentrations in nearby cities can be up to double (NO2) and 74% higher (PM10) when the wind comes from the port. EEA air quality maps showed higher annual mean NO2 concentrations in port areas compared to surrounding regions, with some ports exceeding the 2030 limit value of 20 µg/m³. Annual mean PM10 concentrations were also higher in port areas, with nine ports exceeding the new limit value. The limited number of sampling points makes it challenging to assess trends in NO2 and PM10 concentrations. International shipping emissions significantly contribute to NO2 levels in port cities, as shown by pollution episodes in Antwerpen and Barcelona.
ETC/HE
2024
Air quality and transport behaviour: sensors, field, and survey data from Warsaw, Poland
The present study describes the data sets produced in Warsaw, Poland with the aim of developing tools and methods for the implementation of human-centred and data-driven solutions to the enhancement of sustainable mobility transition. This study focuses on school commutes and alternatives to private cars for children drop off and pick up from primary schools. The dataset enables the complex analysis of interactions between determinants of transport mode choice, revealed choices, and air quality impact. We draw on four data collection methods, namely, (i) air quality and noise sensors’ measurements, (ii) in-person observations of transport behaviours, (iii) travel diaries, and (iv) social surveys. Moreover, all trip data from travel diaries are complemented with the calculated attributes of alternative travel modes. The data produced in the project can be also combined with publicly available information on air quality, public transport schedules, and traffic flows. The present data sets help to open new venues for interdisciplinary analyses of sustainable mobility transition effectiveness and efficiency.
Springer Nature
2024
2011
Air Quality and Healthy Ageing: Predictive Modelling of Pollutants using CNN Quantum-LSTM
The concept of healthy ageing is emerging and becoming a norm to achieve a high quality of life, reducing healthcare costs and promoting longevity. Rapid growth in global population and urbanisation requires substantial efforts to ensure healthy and supportive environments to improve the quality of life, closely aligned with the principles of healthy ageing. Access to fundamental resources which include quality healthcare services, clean air, green and blue spaces plays a pivotal role in achieving this goal. Air quality, in particular, is a critical factor in achieving healthy ageing targets. However, it necessitates a global effort to develop and implement policies aimed at reducing air pollution, which has severe implications for human health including cognitive impairment and neurodegenerative diseases, while promoting healthier environments such as high quality green and blue spaces for all age groups. Such actions inevitably depend on the current status of air pollution and better predictive models to mitigate the harmful impact of emissions on planetary health and public health. In this work, we proposed a hybrid model referred as AirVCQnet, which combines the variational mode decomposition (VMD) method with a convolutional neural network (CNN) and a quantum long short-term memory (QLSTM) network for the prediction of air pollutants. The performance of the proposed model is analysed on five key pollutants including fine Particulate Matter PM2.5, Nitrogen Dioxide (NO2), Ozone (O3), PM10, and Sulphur Dioxide (SO2), sourced from air quality monitoring station in Northern Ireland, UK. The effectiveness of the proposed model is evaluated by comparing its performance with its equivalent classical counterpart using root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2). The results demonstrate the superiority of the proposed model, achieving a performance gain of up to 14% and validating its robustness, efficiency and reliability by leveraging t.
IEEE (Institute of Electrical and Electronics Engineers)
2025