LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

A Decision Support Tool (DST) for the assessment of air quality in the urban area of Athens, Greece was designed and developed. The DST consists of air pollution data from twelve monitoring stations and meteorological data from seven meteorological stations in the greater Athens area, and emphasises on the analysis of air pollution episodes. The DST by being constructed using modern object oriented and user interface techniques confronts successfully the problem of manipulating large amounts of experimental data. In addition the DST performs multidimensional statistical treatment and analysis and can present data and results in graphs and tables for a user defined time window; it is thus considered a useful tool for the assessment of air quality in Athens as well as for the exploitation of the spatial, temporal and long‐term variations of air pollution data and the study of air pollution episodes. The operational test of the DST for Athens demonstrated fast assimilation times and user‐friendly structure. Finally the DST can be used for the assessment of air quality in other urban areas, at the precondition of the availability of air pollution and meteorological data.

The effect of transport on air quality in urban areas of Syria

In recent years, urban air quality monitoring is increasingly important. To monitor air quality in urban areas, wireless sensor networks (WSNs) might be a great tool, because they can automatically collect air-quality data. WSNs are able to provide data with spatiotemporal continuity, so researchers can analyze the data in detail. This paper proposes a self-sustainable air quality monitoring system based on the WSN technology to collect air quality parameters in an urban area. The proposed system uses carbon monoxide (CO) and particulate matter (PM) sensors to monitor CO and PM, and the readings from the two sensors are viewed as air quality indices. The proposed system combines a solar cell and a lead-acid battery as its power supply devices, so the system has the long-tern monitoring capability.

Air quality monitoring and prediction in many industrial and urban areas, it has become one of the most important activities. Owing to different types of pollution, air quality is heavily affected. With increasing air pollution, efficient air quality monitoring models is to be implemented; these models gather data on the concentration of air pollutants. In a proposed approach, to solve three problems- prediction, interpolation and feature analysis, previously these problems were solved using three different models but now in the proposed system can solve these three problems in one model i.e Air Pollutant Prediction. This approach relates to unlabeled spatiotemporal data to enhance interpolation efficiency and air quality prediction. Experiments to test the proposed solution based on the real-time data sources collected by the Karnataka State Pollution Control Board (KSPCB), India. The goal of this research paper is to explore various strategies based on machine learning techniques for monitoring and predicating the air quality.

Recently the intensity and magnitude of air pollutant concentration has grown up rapidly in the troposphere. The air pollution is primarily associated with automobiles and industrial sources Hyderabad, capital of Telangana, is a center of southern part of India for both industrial and commercial activity. It is one of the most populous urban areas with approximately 6.8 million inhabitants and over 73,000 small, medium and major industrial establishments sharing the greater city.Air pollution in India is monitored by the Central Pollution Control Board (CPCB) together with the State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with 7 sensor stations, 248 towns and cities have the air quality network of 591 Air Quality Monitoring Stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing their number to 883 to achieve the target of 1,500 by 2024. For our study due to practical difficulties to collect, analysis the air quality, pollutants data has been collected from CPCB.The dataset contains City, Date, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into 6 zones, air quality monitoring sensors is located at 14 places in and around six zones. For our study we selected 2 stations Newmalakpet and Sanathnagar based on heavy traffic area to analyse the maximum concentration of pollutants such as PM2.5, PM10, NO2, NO3, SO2, CO, for the period of three months ( Dec 2022,Jan, Feb 2023).It was observed at site 1 to site 52PM2.5,range 120 to 249 µg/m3 is very poor as per AQI, PM10 range 101.2 to 171 µg/m3 is moderately polluted as per AQI, NO2 range 7.46 to 32.9 µg/m3 is good as per AQI, NH3 range3.06 to 5.44 is good as per AQI, SO2 is 12.97 to 8.53 µg/m3 is good ,CO 61.83 to 71.9 is severe as per AQI, continuous air quality monitoring is necessary to protect environment and human health

Air is one of the most important elements of life for living things in the world. For humans, the air is an element that is very concerned because it is related to health. In the city of Semarang, the Air Pollution Standard Index (ISPU) at the KLHK station (Ministry of Life and Environment) Semarang City recorded a PM 10 of 7 while the air quality in Jakarta was monitored by the Air Quality Index (AQI) of 67 with parameters in the form of very small pollutant particles with a diameter less than 2.5 micrometres (PM 2.5). The Indonesian government has made efforts to reduce the air pollution index, such as reducing the number of vehicles with odd-even systems, users of environmentally friendly transportation modes such as BRT and Trans Central Java and clearing green lands in the middle of the city. The purpose of this research is how we make a tool that can determine the quality of the air around us and can be carried (portable) anywhere easily. Following the 4.0 industrial revolution that everything has been integrated with the Internet of Things (IoT) technology where the community can find out the air condition in real-time. In this research, later using the prototype method as a test. The main components are sensors consisting of MQ-6 (CO 2 and smoke), MQ-7 (CO, LPG, CH 4 ), MQ135 (Butane, AirQuality), and DHT-11 (Humidity, temperature) From the research that has been done where the air quality in urban areas has a low air quality index by measuring using a prototype consisting of a gas sensor and Arduino microntroller, which has been made to produce an average CO 2 of 25 ppm, CO 2330 which has exceeded the threshold, while NH3 1.23 and C 4 H 10 1120 are still below the threshold. These values are influenced by pollutants generated by transportation such as motorbikes, cars, and land transportation.

Air quality in urban areas located in complex terrain is intimately related to meteorological phenomena that occur in such regions. Major meteorological factors that determine the concentrations of air pollutants are the wind field and the turbulent diffusion. In this study, we investigate the characteristics of the sea breeze and their influence on aerosol particles, ozone and CO2 concentrations around Ahtopol, a small town located at the southern Bulgarian Black Sea coast. Field experiment, performed from 24 to 29 August 2017, comprised measurements of meteorological parameters, aerosol particle number with 6-channel Aerosol Particle counters, atmospheric optical depth (AOD) and total ozone content (TOC) with Microtops II solar photometer and ozonometer, correspondingly. The statistical analysis revealed that onset and cessation periods of the sea breeze were from 9:00 to 10:00 and from 19:00 to 21:00 LST, respectively. The average maximum strength of the sea breeze was 3-4 ms−1. Studying the variation of aerosol particles and CO2, it is concluded that the sea breeze onset is associate with decrease of small particles number, and decrease of CO2 concentration.Air quality in urban areas located in complex terrain is intimately related to meteorological phenomena that occur in such regions. Major meteorological factors that determine the concentrations of air pollutants are the wind field and the turbulent diffusion. In this study, we investigate the characteristics of the sea breeze and their influence on aerosol particles, ozone and CO2 concentrations around Ahtopol, a small town located at the southern Bulgarian Black Sea coast. Field experiment, performed from 24 to 29 August 2017, comprised measurements of meteorological parameters, aerosol particle number with 6-channel Aerosol Particle counters, atmospheric optical depth (AOD) and total ozone content (TOC) with Microtops II solar photometer and ozonometer, correspondingly. The statistical analysis revealed that onset and cessation periods of the sea breeze were from 9:00 to 10:00 and from 19:00 to 21:00 LST, respectively. The average maximum strength of the sea breeze was 3-4 ms−1. Studying the variation ...

Objective: aims to estimate emissions of the main atmospheric pollutants from vehicular sources in the state of Pará, to continue the environmental agenda of monitoring air quality. Theoretical Framework: The growing global concern about air quality in urban areas is driven by the environmental and public health impacts caused by air pollution. The concentration of pollutants from vehicle sources represents one of the main challenges, especially in densely populated regions such as the capital and other major cities in the state of Pará, where the automotive fleet has increased significantly. Method: The methodology used combined "bottom-up" and "top-down" methods to calculate emissions of carbon monoxide (CO), nitrogen oxides (NO) and total hydrocarbons (HC) for different vehicle categories and fuel types. Data on licensed vehicle fleets and fuel consumption obtained from DETRAN-PA and ANP were used. Results and Discussion: The results revealed that gasoline-powered vehicles are responsible for the majority of CO and HC emissions, while diesel vehicles emit the largest amounts of NO into the atmosphere. Motorcycles were identified as the largest source of emissions due to their larger number in the circulating fleet. Our results also highlight the importance of assessing and monitoring vehicle emissions to develop effective air pollution control strategies. Research Implications: The information obtained in this study is unprecedented for the study area, and can therefore support the formulation of environmental policies aimed at reducing emissions of atmospheric pollutants in the state of Pará, thus promoting an improvement in air quality and public health. Originality/Value: This paper makes an unprecedented contribution to national and international literature on environmental pollution in large urban centers. On the eve of the largest climate and environment conference on the planet (COP30) to be held in Pará, Amazonas, this research joins efforts in relation to the monitoring, forecasting and mitigation of air pollution, considered one of the main environmental problems of today.

Air is one of the most important elements of life for living things in the world. For humans, the air is an element that is very concerned because it is related to health. In Jakarta, the Air Pollution Standard Index (ISPU) is ranked second as the capital city of a country with poor air quality. Air quality in Jakarta is monitored by an Air Quality Index (AQI) of 160 with parameters in the form of very small pollutant particles with a diameter of fewer than 2.8 micrometres (PM 2.8). The Indonesian government has made efforts to reduce the air pollution index, such as reducing the number of vehicles with odd-even systems, users of environmentally friendly transportation modes such as MRT, LRT, Trans and clearing green lands in the city centre.The purpose of this research is how we make a tool that can determine the quality of the air around us and can be carried (portable) anywhere easily. By the 4.0 industrial revolution that everything has been integrated with the Internet of Things (IoT) technology where the public can find out the air condition in real-time. In this research, later using the prototype method as a test. The main components are sensors consisting of MQ-6 (CO2 and smoke), MQ-7 (CO, LPG, CH4), MQ135 (Butane, AirQuality), and DHT-11 (Humidity, temperature)From the research that has been done where the air quality in urban areas has a low air quality index by measuring it using a prototype consisting of a gas sensor and Arduino microcontroller which has been made to produce an average CO2 of 25 ppm, CO 2330 which has exceeded the threshold, while NH3 1.23 and C4H10 1120 are still below the threshold. These values are influenced by pollutants generated by transportation such as motorbikes, cars, and land transportation

Spatio-temporal prediction and factor identification of urban air quality using support vector machine

With the quick development of society and industry, air quality has become a grim and global environmental concern. Predicting and rating air quality for many cities remains a significant challenge. Consequently, machine learning algorithms have garnered considerable attention for their potential to address these issues effectively. In this paper, firstly, based on daily air quality data from July 1, 2022 to June 30, 2023 in Lanzhou city of China, five machine learning models, including Bayes Model Averaging (BMA), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are developed to predict the Air Quality Index (AQI) via six major air pollutants (PM2.5, PM10, SO2, NO2, O3 and CO). Secondly, we integrate Bootstrap algorithm into the optimal model, leading to the proposal of the LSTM-Bootstrap algorithm for deriving the standard errors and confidence intervals of the predicted AQI. Thirdly, a cumulative logit model is employed to evaluate and forecast AQI rating. The analysis results indicate that AQI rating is significantly affected by PM10, CO and O3. Additionally, to validate the efficacy of the suggested methods, a similar analysis is conducted on air quality data from Chengdu city for the same period. The findings provide valuable insights for future environmental policies and air quality management strategies.

In smart cities, air pollution has harmful impacts on human physical health and the quality of living environment. correctly predicting air quality is important for developing effective strategies to reduce air pollution and promote healthier, more sustainable environments. Tracking and predicting air pollution is essential for enabling individuals to make wellinformed choices that safeguard their health. Predicting air quality is vital for public health, environmental management, and the development of effective policies. This research focuses on predicting the Air Quality Index (AQI) using machine learning techniques, with an emphasis on improving model efficiency and prediction accuracy. This study compares the performance of two regression algorithms: Linear Regression and Principal Component Regression (PCR) with Decision Tree Regression, across several key evaluation metrics. The performance is assessed using measures such as Mean Squared Error (MSE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE), Median Absolute Error (MedAE), Explained Variance, Adjusted R². The analysis reveals that the PCR with Decision Tree Regression model outperforms Linear Regression in terms of accuracy, as indicated by lower error values and higher explained variance. The superior model also demonstrates better generalization, with more robust metrics like MedAE, which reduces sensitivity to outliers. Overall, the study highlights the advantages of combining principal component regression with decision tree regression for enhanced predictive accuracy.

Abstract— Air quality index (AQI) calculations must be quick and precise since air pollution poses a hazard to both the environment and public health. This work introduces a neural network model-based deep learning approach for AQI estimation. The study makes use of daily measurements of a range of pollutants from different cities, including PM2.5, PM10, NO, NO2, CO, and others. The data is put through pre-processing procedures including modelling and missing value management in order to improve the model's efficacy. A neural network consisting of two hidden layers is trained using the data processing. Using mean squared error (MSE), the model was assessed after more than 150 training runs. Based on pollution data, the research findings demonstrate that the model can forecast AQI. Keywords—Air Quality Index (AQI), Neural Network, Machine Learning, Predictive Modeling, Mean Squared Error (MSE).

Congestion not only has an effect on the network efficiency, but can be linked closely to roadside and background air pollutant concentrations. However, there have been very few studies that have assessed and quantified the impact of different traffic demand management strategies (TDMS) to reduce pollutant concentrations in air quality management areas. This study, funded by the DETR designed, implemented and evaluated a gating strategy upstream of a series of congested junctions which are within a street canyon, in the city of Leicester, UK. The evaluation of the TDMS involved a two month monitoring campaign, covering the period before and after implementation. Traffic flow characteristics, carbon monoxide levels, background levels and meteorological conditions were simultaneously recorded. This study has brought together the achievements of several years of research carried out by the iC research team developing measures and methods for evaluation, congestion identification and linking air pollutant levels with traffic related emission sources.

For the past several years, urbanization and industrialization have been on increase in developed nations, resulting in a huge rise in contaminated air. Citizens and governments have become increasingly worried about the detrimental effects of Air Pollution (AP). Predicting Air Quality (AQ) is a critical step that the government should take because it becoming a major public health concern. The AQ index is a metric for measuring AQ. When fossil fuels such as natural gas, coal, and wood are burned, as well as factories and motor cars, carbon dioxide, nitrogen dioxide, carbon monoxide, and other noxious gases are emitted which leads to AP. This can cause health issues such as cancer, brain impairment, and even death. The forecast of AP allows the government to take preventive actions such as restricting driving hours, partially closing factories, and issuing public service messages. So, the prediction of AP is essential and there is a lot of research being done in this field. This research article reviewed the recent works on AP prediction and present the research using five important sections. In the first section of this review, we will discuss the available methods that can be used to acquire AP data. Second, the processes that can be carried out to pre-process the raw data. Third, the prediction of pollution made use of developing technologies such as Machine Learning (ML) and Deep Learning (DL), and lastly, the evaluation of the model was discussed. The research gap and future steps on the prediction of AP also elaborated. This review will help researchers for a better understanding of the automatic prediction of AP.