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2005
South Africa is the largest national source of industrial atmospheric pollutants in Africa, and the emission of acid-forming pollutants occurs mainly in the eastern Highveld region of the country. However, spatial information on deposition is very sparse beyond the primary emissions zone. Here we quantify wet and dry deposition at four sites from the far northern savanna (Vaalwater) through the grasslands of the interior coal-producing belt of Mpumalanga (Elandsfontein) and the remote KwaZulu Natal Drakensberg mountains (Cathedral Peak) to the fynbos of the southern coast of the country (Knysna), a distance of over 1200 km. Rainwater samples were collected using automated wet-only samplers and analysed for mineral ions and water-soluble organic acids. Wet deposition fluxes were driven largely by rainfall amount rather than differences in chemical composition for three inland sites, with the highest wet deposited sulphur (S) (5.1 kgS/ha/year) and nitrogen (N) (6.9 kgN/ha/year) found in the Drakensberg mountains, greatly expanding the potentially harmful deposition footprint beyond the industrialised Highveld zone. Furthermore, the study period covered the extreme drought years of 2015–2016; hence, wet deposition fluxes could be significantly underestimated relative to more average rainfall years. Dry deposition fluxes, estimated using passive samplers and inferential methods, were far higher at the industrial Highveld site. Overall, total (wet + dry) deposition of S was greatest at the Highveld site (12.0 kgS/ha/year), but the greatest total N deposition (7.0 kgN/ha/year) was found at the remote Drakensberg site. Measured levels of both S and N deposition are well within the ranges found to cause acidification of soils and surface waters in northern hemisphere studies, or changes in vegetation species composition, and could be much higher in more typical, wetter years.
2022
2017
2004
2002
QUILT - Quantification and Interpretation of Long-Term UV-Visible observations of the stratosphere. NILU F
2002
Query-driven Qualitative Constraint Acquisition
Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. Recently, we introduced GEQCA-I, which stands for Generic Qualitative Constraint Acquisition, as a new active constraint acquisition method for learning qualitative constraints using qualitative queries. In this paper, we revise and extend GEQCA-I to GEQCA-II with a new type of query, universal query, for qualitative constraint acquisition, with a deeper query-driven acquisition algorithm. Our extended experimental evaluation shows the efficiency and usefulness of the concept of universal query in learning randomly-generated qualitative networks, including both temporal networks based on Allen’s algebra and spatial networks based on region connection calculus. We also show the effectiveness of GEQCA-II in learning the qualitative part of real scheduling problems.
2024
2007
2009
2015
2013
2015
2023
2023
2006
2013
2017
In this paper, the effect of the lockdown measures on nitrogen dioxide (NO2) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO2, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO2 reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO2 data during the lockdown and the corresponding changes in tropospheric vertical NO2 column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method.
MDPI
2021
2021
2016
2024