Air Pollution Prediction using Machine Learning: A Review

In the effort to achieve accurate air pollution predictions, researchers have contributedvarious methodologies with varying data and different approaches that can be judgedaccurate in their respective contexts. Diverse approaches have been used so far in theliterature to achieve optimal accuracy in the prediction of air pollution. Researchers havealso used different combinations of data such as Meteorological, Traffic and Air Qualitydata. Hence, creating a situation where there are open questions on which of the machinelearning (ML) algorithms or ensemble of algorithms is best suited for various combinationsof data and varying dependent and independent variables. While it is obvious that there isa need for a more optimally performing predictive model for air pollution prediction, it isdifficult to know what combination of algorithms and data is best suited for variousdependent variables. In this study, we reviewed 26 research articles reported recently in theliterature and the methods applied to different data to identify what combination of MLalgorithms and data works best for the prediction of various air pollutants. The studyrevealed that despite the availability of many datasets, researchers in this domain cannotavoid the use of Air Quality and Meteorological datasets. However, Random Forest appearsto perform well for various combinations of datasets

Effect of traffic dataset on various machine-learning algorithms when forecasting air quality

© Emerald Publishing Limited. This is the accepted manuscript version of an article which has been published in final form at https://10.1108/JEDT-10-2021-0554Purpose (limit 100 words) Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic datasets on air quality predictions has not been clearly investigated. This research investigates the effects traffic dataset have on the performance of Machine Learning (ML) predictive models in air quality prediction. Design/methodology/approach (limit 100 words) To achieve this, we have set up an experiment with the control dataset having only the Air Quality (AQ) dataset and Meteorological (Met) dataset. While the experimental dataset is made up of the AQ dataset, Met dataset and Traffic dataset. Several ML models (such as Extra Trees Regressor, eXtreme Gradient Boosting Regressor, Random Forest Regressor, K-Neighbors Regressor, and five others) were trained, tested, and compared on these individual combinations of datasets to predict the volume of PM2.5, PM10, NO2, and O3 in the atmosphere at various time of the day. Findings (limit 100 words) The result obtained showed that various ML algorithms react differently to the traffic dataset despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%. Research limitations/implications (limit 100 words) This research is limited in terms of the study area and the result cannot be generalized outside of the UK as many conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research. Therefore, leaving out a few other ML algorithms. Practical implications (limit 100 words) This study reinforces the belief that the traffic dataset has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form traffic dataset in the development of an air quality prediction model. This implies that developers and researchers in air quality prediction need to identify the ML algorithms that behave in their best interest before implementation. Originality/value (limit 100 words) This will enable researchers to focus more on algorithms of benefit when using traffic datasets in air quality prediction.Peer reviewe

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Future Supply Chains Enabled by Continuous Processing-Opportunities Challenges May 20-21 2014 Continuous Manufacturing Symposium.

This paper examines the opportunities and challenges facing the pharmaceutical industry in moving to a primarily "continuous processing"-based supply chain. The current predominantly "large batch" and centralized manufacturing system designed for the "blockbuster" drug has driven a slow-paced, inventory heavy operating model that is increasingly regarded as inflexible and unsustainable. Indeed, new markets and the rapidly evolving technology landscape will drive more product variety, shorter product life-cycles, and smaller drug volumes, which will exacerbate an already unsustainable economic model. Future supply chains will be required to enhance affordability and availability for patients and healthcare providers alike despite the increased product complexity. In this more challenging supply scenario, we examine the potential for a more pull driven, near real-time demand-based supply chain, utilizing continuous processing where appropriate as a key element of a more "flow-through" operating model. In this discussion paper on future supply chain models underpinned by developments in the continuous manufacture of pharmaceuticals, we have set out; The paper recognizes that although current batch operational performance in pharma is far from optimal and not necessarily an appropriate end-state benchmark for batch technology, the adoption of continuous supply chain operating models underpinned by continuous production processing, as full or hybrid solutions in selected product supply chains, can support industry transformations to deliver right-first-time quality at substantially lower inventory profiles. © 2015 The Authors. Journal of Pharmaceutical Sciences published by Wiley Periodicals, Inc. and the American Pharmacists Association.The authors would like to acknowledge the following for valuable comments and inputs during the preparation of this white paper; Professor Lee Cronin (Glasgow University, UK), Patricia Hurter (Vertex), Mark Buswell (GSK), and Chris Price (GSK). We would also like to acknowledge the support and funding from the UK's Engineering and Physical Sciences Research Council's (EPSRC) Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation (CMAC), and the UK's Department of Business Innovation and Skill's (BIS) Advanced Manufacturing Supply Chain Initiative (AMSCI) funded Project Remedies.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/jps.2434

Influence of snow cover properties on avalanche dynamics

Snow avalanches are a direct thread to many mountain communities around the world and already small avalanches can endanger traffic routes and result in loss of life or property. Their destructive power depends, among other things, on the overall mass and the properties of the flowing snow. The variety of flow regimes in avalanches, ranging from powder clouds to slush flows, is mainly controlled by the properties of the snow released and entrained along the path. So far, the knowledge on how snow conditions affect avalanche behavior is limited and hypotheses are not supported by data. This study aims to provide a first step towards the successful link between snow cover properties and the internal granular composition which in turn affects the flow dynamics of an avalanche. In a first step, the snow cover properties with most relevance for avalanche dynamics, such as run-out distance and front velocity, are identified. For selected large-scale avalanches, the snow conditions were reconstructed using the three-dimensional surface processmodel Alpine3D and the snow cover model SNOWPACK. The data shows that the total mass, mainly controlled by entrained mass, defines run-out distance but does not correlate with front velocity. A direct effect of snow temperature on front velocity, development of a powder cloud and deposition structures could be observed. As a next step field experiments with multiple artificially released avalanches were conducted to quantify the temperature of the flowing snow more accurately and to discuss the magnitudes of different sources of thermal energy. Measured snow temperature profiles allowed quantifying the temperature of the eroded snow layers. Infrared radiation thermography was used to assess the surface temperature before, during and just after the. This data set allowed to calculate the thermal balance, from release to deposition. We found that, for the investigated dry avalanches, the thermal energy increase due to friction was mainly depending on the elevation drop of the avalanche with a warming of approximately 0.5°C per 100 height meters. Contrary, warming due to entrainment was very specific to the individual avalanche and depended on the temperature of the snow along the path and the erosion depth ranging from nearly no increase to 1°C. Furthermore, we could observe that the warmest temperatures are located in the deposits of the dense core. Especially in cases where the described warming processes cause the temperature of the flowing snow to approach the melting point significant differences in the granular composition of an avalanche can occur. Consequently, the granular structures in the deposition zone of avalanches are often intuitively associated with cold or warm avalanches. We conducted experiments on the temperature-dependent granulation of snow and demonstrated that temperature has a major impact on the formation of granules. The experiments showed that granules only formed when the snow temperature exceeded -1°C. Depending on the conditions, different granulation regimes were obtained, which were qualitatively classified according to their properties. All experimentally observed granule classes were reproduced by a discrete element (DE) model that mimicked the competition between cohesive forces, which promoted aggregation, and impact forces, which induced fragmentation

Future supply chains enabled by continuous processing - opportunities and challenges : May 20–21, 2014 continuous manufacturing symposium

This paper examines the opportunities and challenges facing the pharmaceutical industry in moving to a primarily “continuous processing”-based supply chain. The current predominantly “large batch” and centralized manufacturing system designed for the “blockbuster” drug has driven a slow-paced, inventory heavy operating model that is increasingly regarded as inflexible and unsustainable. Indeed, new markets and the rapidly evolving technology landscape will drive more product variety, shorter product life-cycles, and smaller drug volumes, which will exacerbate an already unsustainable economic model. Future supply chains will be required to enhance affordability and availability for patients and healthcare providers alike despite the increased product complexity. In this more challenging supply scenario, we examine the potential for a more pull driven, near real-time demand-based supply chain, utilizing continuous processing where appropriate as a key element of a more “flow-through” operating model. In this discussion paper on future supply chain models underpinned by developments in the continuous manufacture of pharmaceuticals, we have set out; •The significant opportunities to moving to a supply chain flow-through operating model, with substantial opportunities in inventory reduction, lead-time to patient, and radically different product assurance/stability regimes. •Scenarios for decentralized production models producing a greater variety of products with enhanced volume flexibility. •Production, supply, and value chain footprints that are radically different from today's monolithic and centralized batch manufacturing operations. •Clinical trial and drug product development cost savings that support more rapid scale-up and market entry models with early involvement of SC designers within New Product Development. •The major supply chain and industrial transformational challenges that need to be addressed. The paper recognizes that although current batch operational performance in pharma is far from optimal and not necessarily an appropriate end-state benchmark for batch technology, the adoption of continuous supply chain operating models underpinned by continuous production processing, as full or hybrid solutions in selected product supply chains, can support industry transformations to deliver right-first-time quality at substantially lower inventory profiles

Optimization of Fluid Bed Dryer Energy Consumption for Pharmaceutical Drug Processes through Machine Learning and Cloud Computing Technologies

[ES] Los altos costes energéticos, las constantes medidas regulatorias aplicadas por las administraciones para mantener bajos los costes sanitarios, así como los cambios en la normativa sanitaria que se han introducido en los últimos años, han tenido un impacto significativo en la industria farmacéutica y sanitaria. El paradigma Industria 4.0 engloba cambios en el modelo productivo tradicional de la industria farmacéutica con la inclusión de tecnologías que van más allá de la automatización tradicional. El objetivo principal es lograr medicamentos más rentables mediante la incorporación óptima de tecnologías como la analítica avanzada. El proceso de fabricación de las industrias farmacéuticas tiene diferentes etapas (mezclado, secado, compactado, recubrimiento, envasado, etc.) donde una de las etapas más costosas energéticamente es el proceso de secado. El objetivo durante este proceso es extraer el contenido de líquidos como el agua mediante la inyección de aire caliente y seco en el sistema. Este tiempo de secado normalmente está predeterminado y depende del volumen y el tipo de unidades de producto farmacéutico que se deben deshidratar. Por otro lado, la fase de precalentamiento puede variar dependiendo de varios parámetros como la experiencia del operador. Por lo tanto, es posible asumir que una optimización de este proceso a través de analítica avanzada es posible y puede tener un efecto significativo en la reducción de costes en todo el proceso de fabricación. Debido al alto coste de la maquinaria involucrada en el proceso de producción de medicamentos, es una práctica común en la industria farmacéutica tratar de maximizar la vida útil de estas máquinas que no están equipados con los últimos sensores. Así pues, es posible implementar un modelo de aprendizaje automático que utilice plataformas de analítica avanzada, como la computación en la nube, para analizar los posibles ahorros en el consumo de energía. Esta tesis está enfocada en mejorar el consumo de energía en el proceso de precalentamiento de un secador de lecho fluido, mediante la definición e implementación de una plataforma de computación en la nube IIOT (Industrial Internet of Things)-Cloud, para alojar y ejecutar un algoritmo de aprendizaje automático basado en el modelo Catboost, para predecir cuándo es el momento óptimo para detener el proceso y reducir su duración y, en consecuencia, su consumo energético. Los resultados experimentales muestran que es posible reducir el proceso de precalentamiento en un 45% de su duración en tiempo y, en consecuencia, reducir el consumo de energía hasta 2.8 MWh por año.[CAT] Els elevats costos energètics, les constants mesures reguladores aplicades per les administracions per mantenir uns costos assistencials baixos, així com els canvis en la normativa sanitària que s'han introduït en els darrers anys, han tingut un impacte important en el sector farmacèutic i sanitari. El paradigma de la indústria 4.0 engloba els canvis en el model de producció tradicional de la indústria farmacèutica amb la inclusió de tecnologies que van més enllà de l'automatització tradicional. L'objectiu principal és aconseguir fàrmacs més rendibles mitjançant la incorporació òptima de tecnologies com l'analítica avançada. El procés de fabricació de les indústries farmacèutiques té diferents etapes (mescla, assecat, compactació, recobriment, envasat, etc.) on una de les etapes més costoses energèticament és el procés d'assecat. L'objectiu d'aquest procés és extreure el contingut de líquids com l'aigua injectant aire calent i sec al sistema. Aquest temps de procediment d'assecat normalment està predeterminat i depèn del volum i del tipus d'unitats de producte farmacèutic que cal deshidratar. D'altra banda, la fase de preescalfament pot variar en funció de diversos paràmetres com l'experiència de l'operador. Per tant, podem assumir que una optimització d'aquest procés mitjançant analítiques avançades és possible i pot tenir un efecte significatiu de reducció de costos en tot el procés de fabricació. A causa de l'elevat cost de la maquinària implicada en el procés de producció de fàrmacs, és una pràctica habitual a la indústria farmacèutica intentar maximitzar la vida útil d'aquestes màquines que no estan equipats amb els darrers sensors. Així, es pot implementar un model d'aprenentatge automàtic que utilitza plataformes de analítiques avançades com la computació en núvol, per analitzar l'estalvi potencial del consum d'energia. Aquesta tesis està enfocada a millorar el consum d'energia en el procés de preescalfament d'un assecador de llit fluid, mitjançant la definició i implementació d'una plataforma IIOT (Industrial Internet of Things)-Cloud computing, per allotjar i executar un algorisme d'aprenentatge automàtic basat en el modelatge Catboost, per predir quan és el moment òptim per aturar el procés i reduir-ne la durada, i en conseqüència el seu consum energètic. Els resultats de l'experiment mostren que és possible reduir el procés de preescalfament en un 45% de la seva durada en temps i, en conseqüència, reduir el consum d'energia fins a 2.8 MWh anuals.[EN] High energy costs, the constant regulatory measures applied by administrations to maintain low healthcare costs, and the changes in healthcare regulations introduced in recent years have all significantly impacted the pharmaceutical and healthcare industry. The industry 4.0 paradigm encompasses changes in the traditional production model of the pharmaceutical industry with the inclusion of technologies beyond traditional automation. The primary goal is to achieve more cost-efficient drugs through the optimal incorporation of technologies such as advanced analytics. The manufacturing process of the pharmaceutical industry has different stages (mixing, drying, compacting, coating, packaging, etc..), and one of the most energy-expensive stages is the drying process. This process aims to extract the liquid content, such as water, by injecting warm and dry air into the system. This drying procedure time usually is predetermined and depends on the volume and the kind of units of a pharmaceutical product that must be dehydrated. On the other hand, the preheating phase can vary depending on various parameters, such as the operator's experience. It is, therefore, safe to assume that optimization of this process through advanced analytics is possible and can have a significant cost-reducing effect on the whole manufacturing process. Due to the high cost of the machinery involved in the drug production process, it is common practice in the pharmaceutical industry to try to maximize the useful life of these machines, which are not equipped with the latest sensors. Thus, a machine learning model using advanced analytics platforms, such as cloud computing, can be implemented to analyze potential energy consumption savings. This thesis is focused on improving the energy consumption in the preheating process of a fluid bed dryer by defining and implementing an IIOT (Industrial Internet of Things) Cloud computing platform. This architecture will host and run a machine learning algorithm based on Catboost modeling to predict when the optimum time is reached to stop the process, reduce its duration, and consequently its energy consumption. Experimental results show that it is possible to reduce the preheating process by 45% of its time duration, consequently reducing energy consumption by up to 2.8 MWh per year.Barriga Rodríguez, R. (2023). Optimization of Fluid Bed Dryer Energy Consumption for Pharmaceutical Drug Processes through Machine Learning and Cloud Computing Technologies [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19584

Implementation of a Breakpoint Halfway Discretization to Predict Jakarta's Air Quality

Despite the pandemic, Jakarta is one of the most polluted cities in the world. Knowing the daily air quality forecast aids the community, particularly Jakarta residents. Among these is the ability to protect oneself from dangerous air. The multinomial naive Bayes and the decision tree-ID3 methods are popular and perform well. Both of these strategies, however, require categorical variables. This need necessitates the implementation of a discretization technique for numerical variables. The purpose of this study is to predict Jakarta's air quality using the multinomial naive Bayes and decision tree method based on Particulate Matter 10 µg (PM10), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Ozone (O3), and Carbon Monoxide (CO). These continuous variables are discretized in two ways: using all midway breakpoints or halfway mixture breakpoints. The results indicated that the decision tree method with the mixture breakpoints halfway approach performed better than the multinomial nave Bayes method, with an accuracy of 98.90%, a specificity of 98.92%, a sensitivity of 75.00%, a precision of 75.00%, and an F1 score of 97.81%.