The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning

The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers Commun (2023

Application of machine learning algorithms to PM2.5 concentration analysis in the state of São Paulo, Brazil

Dados de monitoramento da qualidade do ar são úteis em diferentes áreas de pesquisa e aplicações, como por exemplo, no estudo da relação da poluição do ar com problemas respiratórios e outros prejuízos à saúde. Dentre os principais poluentes atmosféricos estão: ozônio (O3), dióxido de enxofre (SO2), monóxido de carbono (CO), dióxido de nitrogênio (NO2) e material particulado (MP). O MP é um dos principais objetos de estudos quando se pretende proteger as pessoas da exposição a poluentes. O presente trabalho contribui com a análise da concentração do poluente MP2,5, em 21 estações de monitoramento, observadas pela CETESB - Companhia Ambiental do Estado de São Paulo. Este estudo emprega mineração de dados por agrupamento, um método proeminente para reconhecer padrões e descobrir semelhanças, aspectos importantes para avaliar a poluição do ar, principalmente em uma área geograficamente vasta como o estado de São Paulo, que não segue um único padrão. A técnica de mineração por regras de associação, também aplicada, oferece suporte na análise da relação de poluentes com variáveis meteorológicas, por permitir identificar associações entre elementos que ocorrem juntos, em uma grande variedade de dados. Os objetivos incluem identificar estações com comportamentos semelhantes e explorar a variedade temporal do poluente relacionada aos fatores meteorológicos dominantes nos períodos de alta concentração. O algoritmo de agrupamento, separa de forma automática as estações a partir de médias mensais de concentração de MP2,5 nos anos de 2017 a 2019. Os grupos de estações com maiores índices encontrados do poluente foram os centros urbanos, com emissões por indústrias e veículos e, as estações com índices menores foram as localizadas mais ao interior do estado. Também houve a identificação de um ciclo sazonal nas variações do poluente nos três anos para os dois grupos. Para os meses de maior concentração de MP2,5 a técnica de regras de associação foi aplicada a fim de relacionar temperatura do ar, umidade relativa do ar e velocidade do vento, às concentrações dos poluentes MP2,5 e CO. Os resultados gerados são úteis na análise do perfil temporal e por geolocalização da poluição por material particulado e identifica o comportamento dos fatores meteorológicos que predominam nos períodos de maior concentração.Air quality monitoring data are useful in different areas of research and have varied applications, especially with a focus on the relationship between air pollution, respiratory problems, and other health hazards. The main atmospheric pollutants are: ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and particulate matter (PM). PM is one of the main objects of study when one intends to protect people from exposure to pollutants. This study contributes to the analysis of PM2.5 in 21 stations in the state of São Paulo monitored by the Environmental Company of São Paulo State (CETESB). It employs cluster analysis, a prominent data mining method for detecting patterns and discovering similarities which is important for assessing air pollution, especially in a geographically vast area such as that of the state of São Paulo, which does not follow a single pattern. Another data mining technique (association rules) supports the analysis of the relationship between pollutants and meteorological variables, as it allows identifying changes between elements that occur together, in a wide variety of data. Our objectives include determining stations with similar behaviors and exploring the temporal variety of the pollutant as it relates to the dominant meteorological factors in the periods of high concentration. The clustering algorithm automatically separates stations according to their monthly averages of PM2.5 concentration between 2017 and 2019. The clusters of stations that showed the highest pollution rates essentially included urban centers with emissions by industries and vehicles, while those with the lowest rates were located further inland. A cyclical behavior in pollutant variation was also observed in the three years under study and for both clusters. For the months with the highest concentration of PM2.5, association rule learning was applied to connect air temperature, relative humidity, and wind speed with PM2.5 and carbon monoxide (CO) concentrations. The obtained results are useful to analyze the temporal and geolocation profiles of pollution by particulate matter, since they identify the behavior of the meteorological factors that predominate in periods of greater concentration

Understanding meteorological impacts on ambient PM2.5 concentrations using random forest models in Beijing

Includes bibliographical references.2022 Fall.Policymakers and non-governmental organizations have been implementing policies and interventions designed to reduce exposure to hazardous air pollution. Having knowledge of how non-policy related factors (i.e., meteorology) impact air pollution concentrations in a given study area can better inform longitudinal studies of the effects of the policy on air pollution and health. In this study, we apply a random forest machine learning approach to evaluate how meteorological factors including temperature, relative humidity, wind speed, wind direction, and boundary layer height influence daily PM2.5 concentrations in rural Beijing villages during heating months (January and February of 2019 and 2020). Ten-fold cross validation indicated good model performance with an overall r2 of 0.85 for season 1, and 0.93 for season 2. The models were able to identify variables that were the most important for predicting PM2.5 concentrations both field seasons (relative humidity) and variables that had changes in relative importance between seasons (temperature and boundary layer height). Additionally, examination of one and two-way partial dependence plots as well as interactions through Friedman's H-statistic granted insight into how meteorology variables influence PM2.5 concentrations. Findings from this work provide a basis for adjusting for meteorological variability in important indicators of air quality like PM2.5 concentrations in an ongoing real-world policy evaluation of a province-wide ban on household use of coal for space heating in Beijing, which is critical for isolating (to the extent possible) changes in measured pollutant concentrations attributable to the policy

Air pollution forecasts: An overview

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies

Air pollution exposure assessment in sparsely monitored settings; applying machine-learning methods with remote sensing data in South Africa.

Air pollution is one of the leading environmental risk factors to human health – Both short and long-term exposure to air pollution impact human health accounting for over 4 million deaths. Although the risk of exposure to air pollution has been quantified in different settings and countries of the world. The majority of these studies are from high-income countries with historical air pollutant measurement data and corresponding health outcomes data to conduct such epidemiological studies. Air pollution exposure levels in these high-income settings are lower than the exposure levels in low-income countries. The exposure level in sub-Saharan Africa (SSA) countries has continued to increase due to rapid industrialization and urbanization. In addition, the underlying susceptibility profile of SSA population is different from the profiles of the population in high-income settings. However, a major limitation to conducting epidemiological studies to quantify the exposure-response relationship between air pollution and adverse health outcomes in SSA is the paucity of historical air pollution measurement data to inform such epidemiological studies. South Africa an SSA country with some air quality monitoring stations especially in areas classified as air pollution priority areas have historical particulate matter less than or equal to 10 micrometres in aerodynamic diameter (PM10 μg/m3) measurement data. PM10 is one of the most monitored criteria for air pollutants in South Africa. The availability of satellite-derived aerosol optical depth (AOD) at high spatial and temporal resolutions provides information about how particles in the atmosphere can prevent sunlight from reaching the ground. This satellite product has been used as a proxy variable to explain ground-level air pollution levels in different settings. This thesis main objective was to use satellite-derived AOD to bridge the gap in ground-monitored PM10 across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape). We collected PM10 ground monitor measurement data from the South Africa Weather Services across the four provinces for the years 2010 – 2017. Due to the gaps in the daily PM10 across the sites and years. In study I, we compared methods for imputing daily ground-level PM10 data at sites across the four provinces for the years 2010 – 2017 using random forest (RF) models. The reliability of air pollution exposure models depends on how well the models capture the spatial and temporal variation of air pollution. Thus, study II explored the spatial and temporal variations in ground monitor PM10 across the four provinces for the years 2010 – 2017. To explore the feasibility of using satellite-derived AOD and other spatial and temporal predictor variables, Study III used an ensemble machine-learning framework of RF, extreme gradient boosting (XGBoost) and support vector regression (SVR) to calibrate daily ground-level PM10 at 1 × 1 km spatial resolution across the four provinces for the year 2016. In conclusion, we developed a spatiotemporal model to predict daily PM10 concentrations across four provinces of South Africa at 1 × 1 km spatial resolution for 2016. This model is the first attempt to use a satellite-derived product to fill the gap in ground monitor air pollution data in SSA

Air Quality Research Using Remote Sensing

Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

Proceedings of Abstracts 12th International Conference on Air Quality Science and Application

© 2020 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Final Published versio

Modelling hourly spatio-temporal PM2.5 concentration in wildfire scenarios using dynamic linear models

Particulate matter with aerodynamic diameter < 2.5 μm (PM2.5) is one of the main pollutants generated in wildfire events with negative impacts on human health. In research involving wildfires and air quality, it is common to use emission models. However, the commonly used emission approach can generate errors and contradict the empirical data. This paper adopted a statistical approach based in evidence of ground level monitoring and satellite data. An hourly PM2.5 spatio-temporal model based on a dynamic linear modelling framework with Bayesian approach was proposed in a territorial context with a reduced number of monitoring stations for particulate matter. The model validation is complicated by the fact that all monitoring stations are used in the model calibration. The novel validation method proposed considered both the particulate matter with aerodynamic diameter < 10 μm (PM10) recorded as daily value from 24-h mean every six days as well as the PM2.5/PM10 ratio. Modelling was carried out to provide satisfactorily the exposure level of PM2.5 in a case study of wildfire event.We acknowledge and thank authorities of Red Metropolitana de Monitoreo Atmosférico de Quito (REMMAQ) for providing complementary information to this work. Joseph Sánchez Balseca is the recipient of a full scholarship from the Secretaria de Educación Superior, Ciencia, Técnología e Innovación (SENESCYT), Ecuador.Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i Benestar::3.9 - Per a 2030, reduir substancialment el nombre de morts i malalties causats per productes químics perillosos i la pol·lució de l’aire, l’aigua i el sòlObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.6 - Per a 2030, reduir l’impacte ambiental negatiu per capita de les ciutats, amb especial atenció a la qualitat de l’aire, així com a la gestió dels residus municipals i d’altre tipusObjectius de Desenvolupament Sostenible::17 - Aliança per a Aconseguir els ObjetiusObjectius de Desenvolupament Sostenible::17 - Aliança per a Aconseguir els Objetius::17.18 - Per a 2020, millorar la prestació de suport a la formació per als països en desenvolupament, inclosos els països menys avançats i els petits estats insulars en desenvolupament, amb la perspectiva d’augmentar de forma significativa la disponibilitat de dades actualitzades, fiables i de qualitat, desglossades per grups d’ingressos, gènere, edat, raça, origen ètnic, condició migratòria, discapacitat, ubicació geogràfica i altres característiques pertinents segons el context nacionalObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (author's final draft