Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Optimizing the location of weather monitoring stations using estimation uncertainty

In this article, we address the problem of planning a network of weather monitoring stations observing average air temperature (AAT). Assuming the network planning scenario as a location problem, an optimization model and an operative methodology are proposed. The model uses the geostatistical uncertainty of estimation and the indicator formalism to consider in the location process a variable demand surface, depending on the spatial arrangement of the stations. This surface is also used to express a spatial representativeness value for each element in the network. It is then possible to locate such a network using optimization techniques, such as the used methods of simulated annealing (SA) and construction heuristics. This new approach was applied in the optimization of the Portuguese network of weather stations monitoring the AAT variable. In this case study, scenarios of reduction in the number of stations were generated and analysed: the uncertainty of estimation was computed, interpreted and applied to model the varying demand surface that is used in the optimization process. Along with the determination of spatial representativeness value of individual stations, SA was used to detect redundancies on the existing network and establish the base for its expansion. Using a greedy algorithm, a new network for monitoring average temperature in the selected study area is proposed and its effectiveness is compared with the current distribution of stations. For this proposed network distribution maps of the uncertainty of estimation and the temperature distribution were created. Copyright (c) 2011 Royal Meteorological Societyinfo:eu-repo/semantics/publishedVersio

Impact of COVID-19 pandemic restrictions in urban mobility and air pollution in Lisbon, Portugal

The present work reports the impacts on urban mobility and air quality in Lisbon, Portugal as a consequence of the imposed restrictions to curb the transmission of SARS-CoV-2 virus which causes COVID-19 disease. During the first national emergency period (18-03-2020 to 03-05-2020) the sharp reductions in anthropogenic activities, most importantly road traffic, resulted in generally reduced criteria air pollutant concentration when compared to a homologous baseline from 2013-2019 measured in the six air quality monitoring stations throughout the city. The most negatively impacted air pollutant was No2 with a reduction of 54.35% in traffic stations and 28.62% reduction in background stations. An exception to this trend was the observed O3 concentration increase of 12.89% in traffic stations which is potentially due to changes in the Nox:VOC ratio and reduced O3 titration by NO as a result of sharp decrease of NOx emissions in the usually most polluted city hotspots. This phenomenon raises the need of additional measures to mitigate O3 pollution increases as part of the Lisbon and Tagus Valley air quality improvement plan which aims to reduce NO2 concentrations, namely specific measures for VOC management. Google mobility indicator for local commerce was found to be the main anthropogenic activity indicator for Lisbon with a moderate and positive correlation with NO2 concentration (r=+0.54), whereas the average wind speed was the most relevant natural phenomena contributing to NO2 concentration with a moderate and negative correlation (r=-0.53). A regressor ML pipeline was trained to predict NO2 concentration with the available anthropogenic activity, weather, and air pollutant inputs from March/2020 to March/2021, achieving R2=0.925 on the test set and subsequent feature importance analysis uncovered that anthropogenic features contribute to 41.19% of NO2 concentrations and natural phenomena features contribute to 58.81%.O presente trabalho relata os impactos na mobilidade urbana e qualidade do ar em Lisboa, Portugal, como consequência das restrições impostas para conter a transmissão do vírus SARS-CoV-2, causador da doença COVID-19, onde durante o primeiro período de emergência nacional (18-03-2020 a 03-05-2020) as reduções acentuadas nas atividades antropogénicas, nomeadamente o tráfego rodoviário, resultaram na redução generalizada das concentrações dos principais poluentes atmosféricos medidos nas seis estações de monitorização da qualidade do ar em Lisboa quando comparados ao período homólogo de 2013-2019, sendo o NO2 o poluente atmosférico mais impactado com uma redução média de 54.35% nas estações de tráfego e 28.62% nas estações de fundo. Uma exceção a esta tendência foi o aumento observado na concentração de O3 de 12.89% nas estações de tráfego potencialmente devido a mudanças na relação NOx:COV e redução da ação de redução de O3 por reação com NO como resultado da redução acentuada da concentração de NOx nas zonas habitualmente mais poluídas da cidade. Este fenómeno reforça a necessidade de medidas que mitiguem o aumento da poluição de O3 no âmbito do plano de melhoria da qualidade do ar de Lisboa e Vale do Tejo que visa a redução das concentrações de NO2, nomeadamente medidas específicas de gestão de COV. O indicador de mobilidade da Google para o comércio local em Lisboa foi identificado como a atividade antropogénica mais relevante com uma correlação moderada e positiva com a concentração NO2 (r=+0.54). A velocidade média do vento foi identificada como a atividade natural mais relevante com uma correlação moderada e negativa com a concentração NO2 (r=-0.53). Foi treinada uma ML pipeline para prever a concentração NO2 que teve como entradas os dados de atividade antropogénica, meteorológica e qualidade do ar desde Março/2020 a Março/2021, obtendo R2=0.925 no conjunto de teste. A análise de importância dos atributos identificam as variáveis antropogénicas como responsáveis por 41.19% da concentração NO2 enquanto que as variáveis naturais respondem por 58.81%

Sensor deployment for air pollution monitoring using public transportation system

IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)Air pollution monitoring is a very popular research topic and many monitoring systems have been developed. In this paper, we formulate the Bus Sensor Deployment Problem (BSDP) to select the bus routes on which sensors are deployed, and we use Chemical Reaction Optimization (CRO) to solve BSDP. CRO is a recently proposed metaheuristic designed to solve a wide range of optimization problems. Using the real world data, namely Hong Kong Island bus route data, we perform a series of simulations and the results show that CRO is capable of solving this optimization problem efficiently. © 2012 IEEE.published_or_final_versio

Early wildfire detection by air quality sensors on unmanned aerial vehicles: Optimization and feasibility

“Millions of acres of forests are destroyed by wildfires every year, causing ecological, environmental, and economical losses. The recent wildfires in Australia and the Western U.S. smothered multiple states with more than fifty million acres charred by the blazes. The warmer and drier climate makes scientists expect increases in the severity and frequency of wildfires and the associated risks in the future. These inescapable crises highlight the urgent need for early detection and prevention of wildfires. This work proposed an energy management framework that integrated unmanned aerial vehicle (UAV) with air quality sensors for early wildfire detection and forest monitoring. An autonomous patrol solution that effectively detects wildfire events, while preserving the UAV battery for a larger area of coverage was developed. The UAV can send real-time data (e.g., sensor readings, thermal pictures, videos, etc) to nearby communications base stations (BSs) when a wildfire is detected. An optimization problem that minimized the total UAV’s consumed energy and satisfied a certain quality-of-service (QoS) data rate were formulated and solved. More specifically, this study optimized the flight track of a UAV and the transmit power between the UAV and BSs. Finally, selected simulation results that illustrate the advantages of the proposed model were proposed”--Abstract, page iii

Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing

Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS, to efficiently build fine-grained AQI maps in realtime. Specifically, we first propose the Gaussian plume model on basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a two-dimensional open space like a roadside park, and a three-dimensional space like a courtyard inside a building. Experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm

Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data

© 2019 The increasing rate of urbanization requires effective and reliable techniques for air quality monitoring and control. For this, the Air Pollution Model and Chemical Transport Model (TAPM-CTM) has been developed and used in Australia with emissions inventory data, synoptic data and terrain data used as its input parameters. Since large uncertainties exist in the emissions inventory (EI), further refinements and improvements are required for accurate air quality prediction. This study evaluates the performance of urban air quality forecasting, using TAPM-CTM, and improves accuracy of air pollution estimation by using a two-stage optimization technique to upgrade EI with validation from monitoring data. The first stage is based on statistical analysis for EI correction and the second stage is based on the unscented Kalman filter (UKF) to take into account the spatio-temporal distributions of air pollutant levels utilizing a Matérn covariance function. The predicted nitrogen monoxide (NO) and nitrogen dioxide (NO2) concentrations with a priori emissions are first compared with observations at monitoring stations in the New South Wales (NSW). Ozone (O3) is also considered since at the ground level it represents a major air pollutant affecting human health and the environment. In the second stage, with the improved EI, TAPM-CTM model errors are reduced further by using the UKF to calibrate EI. Results obtained show effectiveness of the proposed technique, which is promising for air quality inverse modeling, an important aspect of air pollution control in smart cities to achieve environmental sustainability