Numerical Air Quality Forecast over Eastern China: Development, Uncertainty and Future

Air pollution is severely focused due to its distinct effect on climate change and adverse effect on human health, ecological system, etc. Eastern China is one of the most polluted areas in the world and many actions were taken to reduce air pollution. Numerical forecast of air quality was proved to be one of the effective ways to help to deal with air pollution. This chapter will present the development, uncertainty and thinking about the future of the numerical air quality forecast emphasized in eastern China region. Brief history of numerical air quality modeling including that of Shanghai Meteorological Service (SMS) was reviewed. The operational regional atmospheric environmental modeling system for eastern China (RAEMS) and its performance on forecasting the major air pollutants over eastern China region was introduced. Uncertainty was analyzed meanwhile challenges and actions to be done in the future were suggested to provide better service of numerical air quality forecast

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Identification of significant factors for air pollution levels using a neural network based knowledge discovery system

Artificial neural network (ANN) is a commonly used approach to estimate or forecast air pollution levels, which are usually assessed by the concentrations of air contaminants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, and suspended particulate matters (PMs) in the atmosphere of the concerned areas. Even through ANN can accurately estimate air pollution levels they are numerical enigmas and unable to provide explicit knowledge of air pollution levels by air pollution factors (e.g. traffic and meteorological factors). This paper proposed a neural network based knowledge discovery system aimed at overcoming this limitation in ANN. The system consists of two units: a) an ANN unit, which is used to estimate the air pollution levels based on relevant air pollution factors; b) a knowledge discovery unit, which is used to extract explicit knowledge from the ANN unit. To demonstrate the practicability of this neural network based knowledge discovery system, numerical data on mass concentrations of PM2.5 and PM1.0, meteorological and traffic data measured near a busy traffic road in Hangzhou city were applied to investigate the air pollution levels and the potential air pollution factors that may impact on the concentrations of these PMs. Results suggest that the proposed neural network based knowledge discovery system can accurately estimate air pollution levels and identify significant factors that have impact on air pollution levels

Rainfall Prediction Using Teleconnection Patterns Through the Application of Artificial Neural Networks

All aspects of human life are, directly or indirectly, affected by climatic processes. This effect is especially noticeable in such fields as agriculture, irrigation, economy, telecommunications, transportation, traffic, air pollution and military industries (Haltiner & Williams 1980). A number of researchers have studied the possibility of forecasting rainfall several months in advance using climate indices such as SOI, PDOI and NPI (e.g. Silverman and Dracup 2000). A well-known atmospheric phenomenon is the Southern Oscillation (SO). The SO is an atmospheric see-saw process in the tropical Pacific sea level pressure between the eastern and western hemispheres associated with the El Niño and La Niña oceanographic features. The oscillation can be characterized by a simple index, the Southern Oscillation Index (SOI). (Kawamura et al., 1998). The Pacific Decadal Oscillation index (PDOI) is the leading principal component of monthly sea surface temperature (SST) anomalies in the North Pacific Ocean north of 20°N (Zhang et al., 1997; Mantua et al., 1997). Trenberth and Hurrell (1994) have defined the North Pacific Index (NPI) as the area-weighted sea level pressure over the region 30°N to 65°N, 160°E to 140°W to measure the decadal variations of atmosphere and ocean in the north Pacific.https://digitalcommons.usu.edu/modern_climatology/1013/thumbnail.jp

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.Comment: 93 pages, 18 figures, under revie

Multifractal characterisation of particulate matter (PM10) time series in the Caribbean basin using visibility graphs

Datos de investigación disponibles en: http://www.gwadair.frA good knowledge of pollutant time series behavior is fundamental to elaborate strategies and construct tools to protect human health. In Caribbean area, air quality is frequently deteriorated by the transport of African dust. In the literature, it is well known that exposure to particulate matter with an aerodynamic diameter of 10 μm or less (PM10) have many adverse health effects as respiratory and cardiovascular diseases. To our knowledge, no study has yet performed an analysis of PM10 time series using complex network framework. In this study, the so-called Visibility Graph (VG) method is used to describe PM10 dynamics in Guadeloupe archipelago with a database of 11 years. Firstly, the fractal nature of PM10 time series is highlighted using degree distribution for all data, low dust season (October to April) and high dust season (May to September). Thereafter, a profound description of PM10 time series dynamics is made using multifractal analysis through two approaches, i.e. Rényi and singularity spectra. Achieved results are consistent with PM10 behavior in the Caribbean basin. Both methods showed a higher multifractality degree during the low dust season. In addition, multifractal parameters exhibited that the low dust season has the higher recurrence and the lower uniformity degrees. Lastly, centrality measures (degree, closeness and betweenness) highlighted PM10 dynamics through the year with a decay of centrality values during the high dust season. To conclude, all these results clearly showed that VG is a robust tool to describe times series properties

The Silk Road agenda of the Pan-Eurasian Experiment (PEEX) program

The Silk Road Economic Belt and the 21st-Century Maritime Silk Road (B&R) aims at facilitating the twenty-first Century economic development of China. However, climate change, air quality and related feedbacks are affecting the successful development of the environment and societies in the B&R geographical domain. The most urgent risks related to the atmospheric system, to the land system and to hydrospheric and cryospheric processes are changing climate - air quality interactions, air pollution, changing monsoon dynamics, land degradation, and the melting of Tibetan Plateau glaciers. A framework is needed in which a science and technology-based approach has the critical mass and expertise to identify the main steps toward solutions and is capable to implement this roadmap. The Pan-Eurasian Experiment (PEEX) program, initiated in 2012, aims to resolve science, technology and sustainability questions in the Northern Eurasian region. PEEX is now identifying its science agenda for the B&R region. One fundamental element of the PEEX research agenda is the availability of comprehensive ground-based observations together with Earth observation data. PEEX complements the recently launched international scientific program called Digital Belt and Road (DBAR). PEEX has expertise to coordinate the ground-based observations and initiate new flagship stations, while DBAR provides a big data platform on Earth observation from China and countries along the Belt and Road region. The DBAR and PEEX have joint interests and synergy expertise on monitoring on ecological environment, urbanization, cultural heritages, coastal zones, and arctic cold regions supporting the sustainable development of the Belt and Road region. In this paper we identify the research themes of the PEEX related Silk Road agenda relevant to China and give an overview of the methodological requirements and present the infrastructure requirements needed to carry out large scale research program.Peer reviewe