Characterization of background air pollution exposure in urban environments using a metric based on Hidden Markov Models

BikeWay: A Multi-Sensory Fuzzy-Based Quality Metric for Bike Paths and Tracks in Urban Areas

“…Actually, when rivers cross urban areas, such quality metrics can be valuable to prevent flooding and other emergencies. In a different work [20], a Markov chain model is used to compute a metric for the air pollution in a city. The idea is to integrate different sources of air pollution, supporting better analyses of how polluted cities are over time.…”

“…Such information is used to estimate air pollution emitted by the vehicles, which can be associated to the roads exploiting GPS coordinates Griego et al [15] 2017 Urban quality of life Exploiting different types of sensors, the "quality" of a city is computed based on environmental data, location/mobility data and perceptual social data provided by the citizens Garau and Pavan [16] 2018 Urban quality of life A great number of variables are combined to compute a unified quality for an area of a city, ranging from Poor Quality to Excellent Quality Carvalho et al [17] 2015 Urban mobility Transportation by public buses is assessed in different cities, highlighting that different cities in Brazil have pursued different goals concerning the perceived quality of the service. Quality indexes such as the Infrastructure Efficiency Indicator (IEI) and the Effectiveness Indicator (EI) are defined Bezerra et al [18] 2019 Urban mobility The Estimated Time to Arrive (ETA) data retrieved from the Uber service can be used to indicate socio-economic inequalities of neighborhoods in a city, providing important information for urban planning O'Briain et al [19] 2018 Environment Different factors in the ecosystem of rivers are evaluated to assess their quality, which may be valuable for smart city scenarios when such rivers cross urban areas Gómez-Losada et al [20] 2016 Environment A metric based on Hidden Markov Models is defined to assess different pollutant gases emissions from different sources, supporting historical analyses in a city zation (WHO), may increase the chances of people to develop some health problems, potentially affecting pedestrians and cyclists [23]- [26].…”

Development of the method of forecasting the atmospheric air pollution parameters based on error correction by neural-like structures of the model of successive geometric transformations

“…The presence of a large number of man-made sources of danger, caused by the functioning of industrial production, containing many permanent sources of air pollution, poses a real threat to humans and the environment [7]. The quantitative environmental risk estimates obtained for a number of large industrial industries are quite high even for normal operation modes [8].…”

Spatial and Seasonal Characteristics of Air Pollution Spillover in China

“…From a spatial perspective, air pollution spillover has the potential to influence distant regions. This distance scale includes intercity spillover [10] and spillover between cities [7]. Wu et al used the GARCH-BEKK model to analyze the air pollution spillover pattern among eight cities in Taiwan Province [11].…”