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In this paper, we have presented a solution for monitoring environmental quality and keeping the air clean, called PAERO-1. It is an IoT-based robotic prototyping used for controlling the air quality based on state-of-the-art NodeMCU microcontroller but not limited to, sterilization, purifying, and measuring air quality in the context of Covid-19 pandemic. The correlated data such humidity, temperature, and volatile organic compound (VOC) are kept into the databank for further processing and decision making. The prototype has been tested with its functions working properly. In addition, this work present how PAERO-1 works with the hardware design and concept flow. Overall, this study is a detail of the IoT robot project to control air quality. [ABSTRACT FROM AUTHOR]

District heating provides several significant environmental advantages over conventional energy sources, including fewer emissions and improved air quality. This research paper focuses on the feasibility of The Hybrid District Heating System for the Northern Region of India. It evaluates the current infrastructure, available renewable energy sources, and the environmental and economic implications of utilizing district heating systems. Additionally, the study looks at the potential benefits associated with such systems, including improved efficiency, reduced emissions, and cost savings. Furthermore, the analysis considers the possible drawbacks of using a district heating system and makes recommendations for increasing the feasibility of such systems in the region. Finally, the paper concludes that The Hybrid District Heating System is a viable and financially sustainable option for the Northern Region of India. [ABSTRACT FROM AUTHOR]

In the face of escalating urbanization, air quality is an integral concern, critically influencing both human health and environmental well-being. As urban areas expand, the imperative for precise, real-time air quality information intensifies. This research introduces the Urban Air Quality Forecasting & Visualization Dashboard, a pioneering platform tailored for Pune city's distinct needs. Capitalizing on machine learning's capabilities, we merged multiple forecasting models, such as ARIMA, LSTM, Facebook Prophet, and TBTAS, to project the Air Quality Index (AQI). Our outcomes elucidate the layered challenges of AQI prediction, especially with the unexpected performance quirks of LSTM and SARIMA's recognized limitations for certain datasets. Nevertheless, through consistent model refinement, our dashboard proficiently dispenses real-time AQI insights, further enhanced by discerning visualizations via Tableau. In amalgamating technology with environmental science, our ambition is to furnish city planners, health agencies, and residents with vital insights, nurturing a forward-thinking paradigm in urban air quality management. [ABSTRACT FROM AUTHOR]

Sewage pump stations SPS usually represent a principal item of sewerage system. As other construction sites, adverse environmental impact of SPS construction phase may be controlled, but the operational phase represents continuous potential source of pollution. The SPS within a city have environmental impact on nearby residential areas through different means and levels. The most important mean is effecting surrounding air quality, especially, with gases like ammonia and hydrogen sulfide. Water quality effect is also potential impact, followed by noise, then land pollution. Impact on culture heritage and landscape are also considered. These impacts enforce restrictions while selecting locations to construct SPS within a city. Attention is paid towards keeping city activities, especially those related to residential areas, at safe distance from the impact. The Iraqi sewerage code contains useful limitations for the SPS distances from a verity of activities related to residential areas. [ABSTRACT FROM AUTHOR]

Road traffic is one of the primary sources of air pollution in urban areas and consequently causing health problems especially to those people who live close to the crowded roads. The objective of this study is to investigate the impact of traffic emission on Airport Road on the surrounding residential areas in Najaf, Iraq. To achieve this objective, a traffic survey, air quality and meteorological parameters were measured during morning times on both a weekend and a weekday at three different distances-50m, 100m and 150m from both sides (A and B) of the road. The air quality results showed that the traffic emission did not cause exceedances on the applicable standards for CO and NO2 at any of the sites during sampling period. The prediction results, using SCREEN model, showed that the maximum predicted concentrations of all three air pollutants (TVOC, CO and NO2) are under predicted for both cases (i.e., USEPA and EURO 6 emission factor) at all three distances from the road, except for NO2 at distances of 50m and 100m for both cases and the predictions of CO and NO2 using USEPA emission factor give much better results than those prediction using EURO 6 emission factor. Based on the results, the CO could be considered as the best indicator for identifying the traffic impact on surrounding residential areas. [ABSTRACT FROM AUTHOR]

On-going electric vehicle adoption in India is based on the reduction in carbon emissions, improvement in the air quality in urban areas and reduction in oil imports. The government of India is working towards shifting to electric vehicles by the year 2030. Nowadays fast charging of Electric vehicles (EVs) also grabbed attention it allows drivers who can't charge at work or at home to own an EV, and it allows extending the EV range for EV drivers on the go and thus it could save a lot of time. This paper presents different charging converter topologies for electric vehicles. schematic and Simulink models, with the simulation results of different converter topologies, are presented. [ABSTRACT FROM AUTHOR]

In recent times, due to the increase in air pollution and its impact on the everyone's health and environment; it has become a very tedious task to monitor and check the air quality. Predicting the air quality is a highly tangled task due to the volatility, particle nature and variables constituting the pollutants. It has become pertinent to check the air quality in rural/urban areas due to the impact it has on people's health and the environment. In this paper, we first do comparative analysis of popular ML(machine learning) method, linear regression, random forest, XGBoost decision tree, k-nearest neighbors (KNN), and L1 and L2 regularization for forecasting pollutant and particulate levels and also the air quality index (AQI) is predicted. Then we apply cross validation and grid search to optimize our random forest model. Finally, we predict the model with Long short-term memory (LSTM). [ABSTRACT FROM AUTHOR]

A prototype model to predict the pollutants level based on air quality index using Modified Random Forest Novel Technique (MRFNT) in comparison with Support Vector Regression (SVR). The proposed model, MRFNT uses the bootstrap and bagging technique on the nonlinear data, the pollutants level is predicted accurately. To predict the air quality, the Air Quality Index CPCB dataset was collected from the National Air Quality Index aided by the Indian government. For better proficiency, bagging error and unbiased data points pre-processed to remove outliers and mix up with continuous categorical variables. The statistical analysis was performed using sample size 20 for each group to perform comparison. From the observed results, MRFNT has 99.96% and SVR has 98.36% accuracy and has better significance of 0.001 for the Confidence Interval (CI) 95% and Significance Value (p<0.05). Based on the result comparison, it shows that MRFNT significantly better than SVR in air quality prediction in various cities in India. [ABSTRACT FROM AUTHOR]

Deteriorating air quality and uncontrolled air pollution can affect climate change as well as human health. The Air Pollutant Index (API) has been used as an indicator to indicate the status of air quality. The objective of this study is to determine the level of air pollution based on the calculation of API as well as to identify clusters for air quality monitoring stations based on the concentration of air pollutants and API values. This study involved 10 air quality monitoring stations from the urban, sub-urban and industrial areas. The data used in this study are the daily concentration of air pollutants namely carbon monoxide (CO), ozone (O3), fine particles (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) from January until December 2011. Median of the pollutant concentrations were used to represent the monthly API for each station. Urban areas had the highest percentage of moderate air quality levels compared to suburban and industrial areas. O3 was found to be the dominant pollutant in urban areas while PM10 is the dominant pollutant in suburban and industrial areas. Fuzzy k-medoid clustering (FKM) is one of the fuzzy clustering algorithms that has a cluster center known as the medoid and can produce soft clusters. The air quality monitoring stations are assigned to the clusters according to the membership degrees ranging in the interval [0,1] and the data points of the station can belong to more than one cluster at the same time with different membership degrees. The degree of memberships indicate the level of confidence that the data points are in a particular group. Two clusters were obtained in which cluster 1 is characterized as a cluster with higher pollutant concentrations and API values compared to cluster 2. [ABSTRACT FROM AUTHOR]

Time series clustering is a data mining technique where it involves grouping of time series data into a few separate clusters. In this paper, a shape-based clustering is applied on time series of PM10 data collected from 60 air quality monitoring stations in Malaysia from 5th July 2017 to 31st December 2018 obtained from Malaysian Department of Environment. k-shape clustering method along with shape-based distance (SBD) were used to cluster the PM10 time series data. Air quality patterns were analysed from the clusters formed. The results show that the clusters formed using the method used are very well separated and mainly influenced by the region and geographical locations instead of the station's categories or man activities at the locations. [ABSTRACT FROM AUTHOR]

Lampung is one of the provinces in Indonesia with an area of 34,623.85 km2 and has 15 districts/cities. Air pollution in Lampung Province has shown a significant increase, causing a decrease in air quality and environmental carrying capacity. This is based on the Lampung Province Regional Regulation Number 20 of 2014 concerning air pollution control. One of the most common inorganic air pollutants is sulfur dioxide (SO2). This study aims to determine the concentration of ambient air quality Sulfur dioxide (SO2) in the dry and rainy seasons and to compare the air quality in 15 districts in Lampung Province from the results of measurements using the Passive sampler method. Sampling was carried out in the rainy season (January) and dry season (August). The mean concentration was used as the annual mean value, and then data analysis was performed using a two-way ANOVA. Based on the two-way ANOVA test, the researchers found that the concentration of SO2 was not significantly different (p-value >α) between the average concentration of SO2 in each district/city for two consecutive years. This indicates that there is no difference in the average SO2 between the dry season and the rainy season, in other words that the dry and rainy seasons have no effect on SO2 pollutant levels. [ABSTRACT FROM AUTHOR]

Generally, the term "air contamination" describes the discharge of foams into the sky that is damaging to both individual persons and the atmosphere as a whole. It can be defined to be one of the most harmful intimidation humanities have ever encountered. It causes harm to animals, crops, forests etc. Machine learning techniques must be used to anticipate air quality from pollution in the transportation sectors to avoid this issue. As a result, evaluating and forecasting air quality has grown in consequence. The primary goal of this work is to attain better accuracy in reducing Metallic Hydrocarbons using machine learning algorithms and compare with other one existing. As a result, it is now more important than ever to analyze and forecast air quality. The goal is to examine machine knowledge-based methods for predicting air quality with the highest degree of accuracy. To gather various types of evidence and conduct comprehensive analysis, including bivariate examination, variable identification, multivariate analysis, univariate analysis, handling of misplaced values, data authentication, data cleaning/training, and data representation, the entire dataset will undergo analysis using supervised machine learning techniques (SMLT). Our study provides a detailed guide for assessing the sensitivity of model parameters in terms of precision performance in forecasting air quality contamination. Various Machine learning-based method are offered to predict the accuracy in quality contamination. To put forth a solution based on machine learning that can forecastthe Air Quality Index value with the greatest degree of accuracy by comparing several classifying machine learning systems. As per the experimental results, the proposed system is considered as the best approach to reduce contaminants from industrial air. Here the Convolutional Neural Network is used to Reduce Metallic Hydrocarbons more accurately to all other algorithms. Additionally, using the provided transport traffic department dataset, compare and converse the accuracy of various machine learning algorithms as well as GUI- based user interface air quality prediction by attributes. [ABSTRACT FROM AUTHOR]

This study emphasis on enhancing the detection of air quality pollutants using Novel Convolutional Neural Networks (CNN) compared with Support Vector Machines (SVM). The research study consists of two groups, Novel CNN (N=10) and SVM (N=10), which are applied to the dataset of India Air Quality (Kaggle.com) with the total samples of 17481. The iteration for each group is 10 and was calculated using ClinCalc software with α= 0.05 and a pretest power of 0.8. The proposed Novel Convolutional Neural Networks Technique has the potential to improve air quality detection. Novel Convolutional Neural Networks (95.34%) has increased air quality detection accuracy over SVM (91.2010%) with a significant value of 0.068 (Independent sample t-test, p<0.05). This means that the two methods have a statistically significant difference. The study revealed that compared to the accuracy of SVM, Novel CNN is more accurate in detection of air quality pollutants. [ABSTRACT FROM AUTHOR]

Ground-level ozone (O3) is a major air pollutant that can have significant impacts on human health, ecosystems well-being, and agricultural productivity. This study aims to map the spatial distribution of extreme O3 in Peninsular Malaysia using stationary and nonstationary Generalized Extreme Value (GEV) models. The models are applied to air quality data collected from 24 air monitoring stations across the region between 2000 and 2016. The stationary GEV model assumes that the distribution of extreme O3 values is constant for all parameters while the nonstationary GEV model allows for cyclic effect on location parameters to capture trends or changes in the underlying distribution while other parameters remain constant. The results show that both stationary and nonstationary GEV models perform well in terms of goodness-of-fit statistics using probability plotting method. Maps generated by the stationary and nonstationary GEV model reveal significant spatial variation in extreme O3 concentrations across the region, with hotspots in urban areas and near major industrial facilities. The findings provide important information for policymakers and other stakeholders working to mitigate the impacts of air pollution in Peninsular Malaysia and demonstrate the use of extreme value theory techniques in modelling spatial distribution of extreme environmental events. [ABSTRACT FROM AUTHOR]

Covid-19 was first discovered in Jakarta in February 2020 and began to become epidemic around mid-March 2020. The government implemented a Large-Scale Social Restriction (LSSR) policy as an effort to suppress the widespread of viruses. This policy causes a decrease in public mobility 2020 which is believed to cause air quality improvement. This study was conducted to determine changes in air quality using ground surface measurement data and satellite products (OMI, AIRS, and MERRA-2) on the atmospheric gas parameters (NOx, CO, and SO2) for the period of 2019-2020. Potential sources of emissions due to changes in activities in DKI Jakarta were quantified and their relationship to air quality improvements was analyzed. The emission inventory was carried out using the Workbook of Atmospheric Brown Cloud-Emission Inventory Manual. In 2020 the results of air quality monitoring showed a decrease for CO and NO2 parameters, except SO2. The calculation of potential emission sources showed significant decrease in activity for the power generation sector, manufacturing industry, and transportation of 50.5%, 37.2%, and 56.9%, respectively. Fuel consumption in the residential sector did not decrease due to work from home with the emission load value increasing by 2.9% in 2020. The largest contributor of emissions comes from the on-road transportation sector. [ABSTRACT FROM AUTHOR]

The project Air Quality Monitoring System using IOT aims to provide a system to let individuals monitor the air quality in their immediate environment or at any location connected over the same LAN where the air quality must meet a certain level. With a Wi-Fi connection, the hardware could be installed almost anywhere, and anyone could check the readings. The gear has an internet connection, so it can be checked on from anywhere connected over the same network. Harmful gases in our surroundings can be monitored and kept track of in this project. [ABSTRACT FROM AUTHOR]

Air pollutants play a significant role in assessing the ambient air quality of a region. The precise estimation of air quality is necessary to safeguard the common population from the ill health effects of such pollutants. Aerosols are particles, either solid or liquid, in gaseous medium, which makes impacts on climate as well as human health. The size of the particles of a mono-disperse aerosol is defined by the particle's aerodynamic diameter. It is vital to describe these size distributions using statistical methods due to the vast range and since the physical characteristics of aerosols greatly depend on particle size. The risk that the inhaled particles pose depends on their chemical composition and where they deposit in the respiratory system. Therefore, to adequately assess the health risks of aerosols, it is vital to understand the deposition of particles in the lungs. The efficient inhalation of pharmaceutical aerosols also depends on this kind of comprehension. This project attempts to understand the distributions followed by the aerosols in each month and to estimate the dominating size range of particles seasonally as it would help in taking measures against the deposition. Further, the multi-modal nature of urban aerosol mass distribution for Bhopal using log-normal distribution curves and the empirical equations using the International Commission on Radiological Protection (ICRP) model; the regional and total deposition in the human lungs are estimated. [ABSTRACT FROM AUTHOR]

World Health Organization (WHO) declared a global health emergency due to the new coronavirus disease (COVID-19) which has spread uncontrollably around the world1. To limit the spread of the pandemic, governments in countries around the world have imposed various restrictions, which have led to a reduction in the movement of people, a decrease in transportation (roads and flights), and even a halt to industrial activities2. The Indonesian government is pursuing various policies to avoid an increase in the spread of Covid-19 since the case was detected in Indonesia. One of the government's policies is to issue regulations so that people reduce activities outside the home known as Large-Scale Social Restrictions (PSBB). This study analyzes data from the results of air quality testing in Yogyakarta from the Ministry of Environment (KLH), the number of students, teachers, lectures, and government employee working in schools, university, and governments. The data is data in 2020 at the time of the Covid-19 Pandemic and is compared with data in 2019 before the Covid-19 pandemic occurred. Based on the circular letter of the Governor of the Special Region of the employee on duty is regulated with a composition of at least 50% working in the office and a maximum of 50% working at home. There are 538,605 students at the high school and university levels in Yogyakarta who has experienced the impact of the implementation of the PSBB. The regional employees in the Special Region of Yogyakarta is a 50% decrease. The Community Mobility Trends Report shows that there has been a significant decline in social activities outside the home since late February. The decrease in mobility and the decrease in community activities outside the home affect the air quality in the Yogyakarta City environment. This can be proven by an increase in ambient air quality in Yogyakarta City in 2020. This is indicated by a decrease in SO2, NO2, and CO levels in the air in the Yogyakarta environment. Based on data from the Yogyakarta City Environment Service, there was a decrease in SO2 by 45%, NO2 by 68.7%, and CO by 12.5%. [ABSTRACT FROM AUTHOR]

The negative impact of air pollution on human health is substantial. Chest discomfort, haemorrhage, asthma, emphysema, and other respiratory issues may be caused by both short-term and long-term exposure to air pollution. According to the World Health Organisation, air pollution occurs both inside and outdoors and is responsible for around 7 million deaths annually throughout the globe. High levels of air pollutants, beyond WHO standards, are inhaled by about 92% of the global population. Predictions of air quality techniques made so far are both expensive and wrong. Current methods for predicting air quality have data size limitations. In the past, researchers have attempted to measure air quality using methods like probability and statistics, but these approaches are notoriously difficult to forecast and hence, provide inaccurate results. Advancements in machine learning algorithm technology have the potential to be very fast. Consequently, air quality forecast needs improvement for a healthier atmosphere. Using supervised machine learning techniques, this study builds an AQI prediction model using past data. It then presents an inexpensive, fast, and accurate IoT system for air quality prediction using real-time sensors and new assessment and prediction features. The air quality predictions were made using decision tree and supervised machine learning methods. Decision tree methods come in two varieties: classification trees, which use the data's class as their prediction result, and regression trees, which use a real number as their prediction result. Classification and regression tree, or CART, is the name given to this kind of graph. Lastly, surveys urge the public to receive air quality alerts, especially in locations with limited data, and air quality forecasts are developed and validated with a 96 percent accuracy rate. The Meteorological Department may depend on the air quality prediction model that is based on machine learning for forecasting the status of the air and its quality. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine air quality and pollutant levels by employing a quantitative-descriptive method conducted at the BMKG Pesawaran Climatology Station from 2019 to 2021. This study's sample and population were daily rainfall data for 2019-2021 and air quality in Rajabasa District, Bandar Lampung. The variables were measured using pH and conductivity measuring instruments (pH meters and conductivity meters), which showed data directly and concretely. Interview, observation, and documentation techniques were utilized to obtain data. The researchers discovered that rainfall was inversely connected to conductivity values by gathering data from the BMKG Pesawaran Climatology Station. Furthermore, there was no significant relationship or influence between rainfall and pH. The conductivity and pH values did not affect one another. However, these three (rainfall, conductivity, and pH) are predictors of air quality and serve as good and healthy air parameters for the planet and all its contents. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is proposed for monitoring aquatic environments, consisting of data monitoring nodes, database stations, and a remote monitoring center. This system is highly versatile and suitable for comprehensive monitoring of various water environments, including shallow or deep groundwater, lakes, rivers, swamps, and reservoirs. The system's database foundation is broad and standardized, allowing for easy addition of new input data by users. The graphical user interface (GUI) of the system includes an Edit sub-module, empowering the system administrator to modify water quality data for different rivers. The study aims to utilize neural network approaches to enhance the development of advanced monitoring systems for river water and air quality. These systems will be displayed in the GUI for online testing and prediction, facilitating monitoring, diagnosis, and control. The study demonstrates the successful implementation of pH value monitoring in an artificial lake using technology. By utilizing the GSM network, the monitoring station can send encrypted text messages and receive responses to monitor the waters. The device used in the system is capable of measuring pH values between 1 and 14 with an accuracy of 0.05 pH units. With the neural network achieving 95% prediction and validation, the monitoring system meets the requirements for a variety of water environments. For different water quality scenarios, it is essential to place suitable sensors in the contract to obtain various parameters. As a result, the monitoring system holds promising potential for broad application in environmental monitoring. [ABSTRACT FROM AUTHOR]

This study compares three different deep learning approaches for forecasting the air quality time series data. All the approaches were based on the convolutional neural network (CNN), which is one of the deep learning algorithms. The first approach, known as the univariate CNN (UCNN), is based solely on the time series past values. Meanwhile, the second approach, known as the multi-parallel CNN (MPCNN), will use the same variable time series from different observations as the input for the model. The last approach is the multivariate CNN (MCNN), where the forecast will incorporate other variables' time series as the input for the model. The time series data used in this study are the hourly particulate matter, PM10 and PM2.5, concentrations from eight air quality monitoring stations in Peninsular Malaysia. The duration of the time series is from July 2017 until June 2019. The results show that the best air quality predictive model is a model which is built using UCNN since it shows the highest accuracy for both time series PM10 and PM2.5. It is also shown that the forecast will have better accuracy if shorter past values are used as the input for the model. [ABSTRACT FROM AUTHOR]

The weather and air quality monitoring system provides valuable information to support many activities. This paper presents the development and implementation of the weather and air quality monitoring system based on the Internet of Things (IoT) platforms. The developed system adopts the low-cost weather and gas sensor and the small, powerful Arduino Nano 33 IoT microcontroller. Furthermore, the solar panel and battery system are used as the energy resources for the developed monitoring system. Two popular IoT platforms, namely the ThingSpeak and Blynk, are employed as the IoT cloud servers, where the ThingSpeak is mainly used for saving, displaying, and analyzing historical data, while the Blynk is primarily used for online monitoring based on the mobile application. The experimental results show that the electronics and sensors modules consume the power of 1.3 Watt, and the IoT module requires a data transmission size of 429 KB per transmission. The preliminary evaluation shows that by operating the system at a time interval of 180 minutes, it achieves a ratio of charging time to discharging time of 0.33, which offers a promising result for a self-powered and green energy system. [ABSTRACT FROM AUTHOR]

The implementation of Community Activities Restrictions Enforcement (PPKM) during the Covid-19 pandemic reduced activities in the transportation sector. This paper presents the measurement of Carbon Monoxide (CO) concentrations during the PPKM in Makassar City, Indonesia. The samples were collected weekly from August to September 2021 from two traffic-heavy roads using Tedlar bags and a vacuum pump and then analyzed using the Non-Dispersive Infrared method (NDIR). Results showed that the average concentrations of CO on the first street in the morning, midday, and afternoon were 826.63±83.34 µg/m3, 685.65±57.43 µg/m3, 861.6±59.79 µg/m3, respectively; and on the second street were 875.46±77.80 µg/m3, 741.80±62.95 µg/m3, 833.04±44.80 µg/m3, respectively. These CO concentrations measured during the PPKM are lower than those measured before the PPKM on the same sites, reported in another paper. The concentrations are also much lower than those of the Air Quality Standard in Makassar, 30,000 µg/m3. This finding implies that similar policies to reduce traffic and pollution, such as car-free-day, should be enacted to improve air quality. [ABSTRACT FROM AUTHOR]

An investigational study on the ambient air quality of Aligarh city at six different locations designated as; Sir Syed Nagar, Jamalpur, Dodhpur, Center Point, Numaish Ground, Chungi was performed. Six primary and important locations of Aligarh city were chosen to monitor the quantities of key pollutants like PM10 and PM2.5. The outside areas were susceptible to extraordinary RSPM levels because of prevalent town ambient air excellence of Aligarh city and also due to high road traffic circulation at Dodhpur, chauraha and centre point. The concentration of PM2.5 at Aligarh city atmosphere was observed from 3.5 µg/m³ to 15 µg/m³ and PM10 level of concentrations at all the designated locations varied from 75 - 250 µg/m³. [ABSTRACT FROM AUTHOR]

The high levels of pollution and their inadequate management have emerged as one of the national issues in the environmental area. The development of an integrated air quality and environmental radiation monitoring system based on low-cost sensors and real-time monitoring is needed for the early identification of environmental conditions efficiently. The initial stage of this research activity is focused on assessing air quality parameters, especially the concentration of ambient air particulate masses based on low-cost sensors and their verification with filter-based sampling methods. The activity was carried out in July-August 2022, in the Bandung Nuclear Area. The results of the low-cost sensor method obtained showed that the average mass concentration of airborne particulates with a diameter of less than 1 µm (PM1.0), 2.5 µm (PM2.5), and 10 µm (PM10) was 34.50±10.08 µg/m3, 54.36±17.90 µg/m3, and 62.01±19.30 µg/m3, respectively. Evaluation of the low-cost results obtained was verified using a filter-based sampling method, especially for PM2.5 fine air particulate parameters. Based on these preliminary data obtained, the comparative results of the two methods were obtained very well with a coefficient of determination value of 0.95 and an average value of comparison to the two methods of 0.83±0.08 excluding the overestimated PM2.5 concentration. The results obtained will be used as a reference in the development of low-cost sensors for multi-pollutant air monitoring systems to provide comprehensive information as an early warning system in improving air quality in Indonesia. [ABSTRACT FROM AUTHOR]

Air pollution is usually described as the release into the atmosphere of contaminants which are harmful to human health and the environment. It may be described as one of the most serious risks human beings have ever faced because to the growing more catastrophic problem of air pollution in the globe. A description is provided of how our air quality sensor system is designed, implemented and optimised, which provides a live and fine grain air quality map of the area under surveillance. The average joint failure in the generated real-time air quality map should be maintained to a minimal when unmeasured data values as part of the objective are deducted. In terms of studying the quality issues, Big Data Technology has been recommended to be utilised in order to organise the air quality sensing equipment sensing tasks appropriately. In addition, the prediction of air quality based on climatic conditions is explored using various machine learning techniques for the data given. [ABSTRACT FROM AUTHOR]

In recent times, due to the increase in air pollution and its impact on the everyone's health and environment; it has become a very tedious task to monitor and check the quality of air. Predicting the quality of air is a highly tangled task due to the volatility, particle nature and variables constituting the pollutants. It has become pertinent to check the quality of air in rural/ urban areas due to the impact it has on people's well-being and the environment. In this paper, we first do comparative analysis of popular machine learning method, decision tree, linear regression, k-nearest neighbors (KNN), random forest, XGBoost and L1 and L2 regularization to forewarning pollutant and particulate levels and to forecast the air quality index (AQI). Then we apply cross validation and grid search to optimize our random forest model. Finally, we predict the model with Long short-term memory (LSTM). [ABSTRACT FROM AUTHOR]

According to the 2006 research report data of the Lembaga Penerbangan dan Antariksa Nasional (LAPAN) and the findings of the Balai Besar Teknologi Kesehatan Lingkungan dan Pengendalian Penyakit (BBTKLPP), the oxygen (O2) and carbon dioxide (CO2) levels on Gili Iyang Island reached 20.9%–21.5% and 302 ppm – 313 ppm, respectively. In comparison to oxygen levels in other regions, O2 concentrations are relatively higher. This investigation determined the air quality on Gili Iyang Island by an examination. Using O2 and CO2 meters, O2 and CO2 concentrations were measured at 77 sites over the island's land and water. The average O2 concentration is 20.02%, whereas the average CO2 concentration is 475.68 ppm. The measured findings suggest a reduction in O2 concentration and an increase in CO2 concentration. Based on the results of the visualization using the 3D field with the color cell map approach, the waters of the southern portion of the island have the highest O2 levels, while the middle of the island has the highest CO2 levels. Multiple variables contribute to the increasing and decreasing CO2 concentrations. The installation of a diesel power plant in 2017 and the increase in the number of motor vehicles on Gili Iyang island are among the contributing reasons. These have been delivering adverse implications for the island's ecosystem. [ABSTRACT FROM AUTHOR]

Now a days, air pollution is a critical environmental issue in developing countries due to urbanization and industrialization. The pollution in air causes severe health issues for human beings, affects plants and animals due to acid rain, creates loss in economy, agricultural production and commercial production. In order to prevent or control it, the air quality forecasting necessitates. Accurate air quality prediction is a prominent task to be done to control the pollution level and to take countermeasures for securing the human life. The complex, uncertain and non-linear nature of the air quality pollutants impose complications in achieving accurate forecasting. The advent of artificial intelligence techniques helps to improve the air pollution forecasting results compared to traditional statistical methods at the great extent. This paper focuses the complications of air pollution, significance of air quality prediction, air quality index, and reviews the applications of machine learning and deep learning methods in air quality prediction. Literature shows that the machine learning and deep learning methods along with feature engineering and optimization techniques guarantees a promising air quality prediction. [ABSTRACT FROM AUTHOR]

In the world of modern era we find many techniques for enhancement of reliability in distribution networks and meeting the consumer demand by provision of quality and incessant power supply. Amidst the various techniques, in this study, we opt for demand response (DR) and charging/discharging of electrical vehicles (EV's) are evaluated to ameliorating reliability for radially distributed power system by adopting teaching learning based optimization (TLBO) algorithm. This analysis is considered due to the contribution of technologies like are DR and EV's for reducing the usage of conventional sources and enhancing the air quality. Here in this paper we are adopting reliability evaluation by considering energy expected not supplied (EENS) and loss of load expected (LOLE)where the characteristics of distribution network contains load and voltage profile. This paper provides a new optimization approach for finding the optimal solution. [ABSTRACT FROM AUTHOR]

Aerosols are mainly situated within the planetary boundary layer (PBL), affecting radiation, atmospheric stability, clouds, and precipitation. Aerosols, the PBL, and clouds may be coupled, influencing each other's development and further influencing meteorology and the atmospheric environment. The impacts and their interactions may be significant enough to affect weather and climate, depending on their states and coupling relationships. This study aims at understanding the fundamental processes governing the interactions between boundary-layer clouds and aerosols. By examining the development of boundary-layer clouds and aerosol transport in the context of surface-cloud coupling, we attempt to advance the understanding of their impacts on the development of convective clouds. To this end, we have developed algorithms for identifying the PBL height and cloud-surface coupling and have used them to investigate the fundamental mechanisms of aerosol-PBL-cloud coupling and their interactions, furthering our understanding of their joint effects on the development of convective clouds and air quality. [ABSTRACT FROM AUTHOR]

The Earth, a complex and interconnected system, faces an imminent threat from climate change, exacerbated by escalating carbon emissions. The Philippines addressed air quality concerns with the Clean Air Act of 1999, emphasizing mobile sources like vehicles. This paper proposed a catalytic converter prototype incorporating zeolite to assess its efficiency in reducing carbon emissions. Employing an experimental approach, the study utilizes t-tests as a statistical tool to compare carbon emissions between vehicles with and without the zeolite-based catalytic converter and value analysis and value engineering to optimize the production process and reduce costs. Based on the obtained results, "Z-Lite' is a profitable alternative exhaust filter for diesel engines, showcasing its potential to reduce carbon emissions significantly. The statistical analysis, yielding a low p-value, supports the hypothesis that zeolite filters effectively reduce carbon emissions. With over 100 emission centers in the Philippines, the marketability of Z-Lite in Marikina City underscores its practicality in addressing environmental concerns and promoting sustainable practices in the transportation industry. The integration of value analysis and value engineering ensures that it is both environmentally friendly and affordable, making it a promising and sustainable choice for mitigating the environmental impact of diesel engine emissions. [ABSTRACT FROM AUTHOR]

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Copyright of Proceedings of the International HVAC&R Congress is the property of Union of Mechanical & Electrotechnical Engineers & Technicians of Serbia (SMEITS) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Poor air quality is caused by fuel combustion residue, so it is necessary to compile an Air Pollutant Index (API) as information on air quality at a certain location and time. The parameters used to determine the API category are SO2, PM10, NO2, O3, and CO, these parameters are also used as predictive variables. The purpose of this study was to assess the performance of the combination method of Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) on air quality predictions based on 5 API's parameters. The data used comes from the DKI Jakarta Provincial Environmental Agency for 2017-2019 with a lot of data, which was 1075 data, split data was used to divide 70% for training data and 30% for testing data. The prediction results showed the accuracy value of the ANN-PSO was 88% with a sensitivity value of 0.67 and a specificity value of 0.95. The results were compared with the ANN method for algorithm performance analysis, the results obtained from ANN were an accuracy value was 79% with a sensitivity value of 0.62 and a specificity value of 0.91. From the results, it can be concluded that the ANN-PSO method is quite good in predicting air quality based on the API value. [ABSTRACT FROM AUTHOR]

Urban Green Space is one of the important areas in urban areas which is characterized by the presence of vegetation and is a factor in determining temperature and air quality in urban areas. It plays an important role in balance, comfort, health, sustainability, and improving the quality of human life. Research on deep learning using satelliteimagery, especially in urban green space segmentation, is still very rare. Therefore, we conducted research with deep learning in urban green space segmentation using satellite imagery. We made improvements from previous researchersby using U-Net architecture to segment urban green space with planet scope satellite images in Sleman Regency and Yogyakarta City. The dataset that we have has a resolution of 3m and 4 bands, namely Red, Green, Blue and Nir. From the results of the training model, our method gets the smallest loss with a value of 0.012 for training and 0.0006 for validation. Our method obtains an IoU score of 90.68% which outperforms previous studies with our dataset. [ABSTRACT FROM AUTHOR]

People are significantly impacted by poor air quality. Millions of fatalities are attributed to health problems and air pollution. To regulate and improve air quality, the World Health Organization and the Environmental Protection Agency have created a number of rules and standards. Recently, several writers proposed low-cost instruments for quickly determining air quality. the plethora of low-cost methods for gathering sensor data and evaluating air quality. In this work, a remote safety monitoring system for temperature, carbon monoxide, and humidity has been designed and implemented as a single tool. The OLED display of the measuring device and the LCD display of a remote monitoring system both display the measurement results. The ESP32 microcontroller and LoRa wireless technology are both used by this device. While the MQ-7 sensor is used to track carbon monoxide levels, the DHT22 sensor measures temperature and humidity. Testing The AS8700A Carbon Monoxide Meter and the 4000 NV Pocket Weather Tracker, both of which are used in the Environmental station, are calibrated using equipment that complies with industry standards. An alert will be sent if irregularities in the recorded parameters are found (buzzer and blinking LED). Patients, staff members, and health care providers can use this device to inspect their surroundings and do remote monitoring. [ABSTRACT FROM AUTHOR]

Ventilation plays a key role in maintaining the indoor environmental quality of buildings. This contribution focuses on the significance of ventilation intensity as a crucial indicator of air quality within buildings. The aim is to analyze the impact of ventilation intensity on various aspects of the indoor environment, including thermal comfort, pollutant concentrations, and overall occupant comfort. The research is centered around methods for measuring ventilation intensity and ways to optimize this value to achieve optimal outcomes in the realm of air quality and comfort. Emphasis will be placed on technological innovations that enable more efficient and intelligent ventilation control, including the use of sensors, automation, and energy-efficient systems. The results of this study could have a significant impact on building design and operation, especially in the context of growing awareness of the importance of a healthy indoor environment and the need for energy efficiency. Ventilation intensity is becoming a pivotal factor in achieving these goals, and its analysis and optimization are essential for the future of sustainable buildings. [ABSTRACT FROM AUTHOR]

Landfills of municipal waste are important sources of methane emissions. The surface of the landfill area is the interface between the waste layer and the atmosphere. By creating a biocover cover layer, it is possible to reduce methane emissions that would refer to air quality. This innovative biocover technology aims to curtail methane emissions from various sources. The research delves into the effectiveness of this biocover in not only mitigating methane emissions but also its potential influence on nitrous oxide emissions. Through a combination of laboratory experiments, the study unravels the intricate dynamics between methane and nitrous oxide emissions in the context of the methane-reducing biocover. The study created a laboratory biocover experiment. Interaction between methane and nitrous oxide emissions was studied using methane-reducing biocover. The experiment was launched on 6 June 2022. This was done by creating tube columns with a diameter of 160 mm and a height of 1500 mm. The biocover used consisted of 60% of fine-fraction waste, 20% of soil and 20% of compost. Measurements of methane and nitrous oxide emissions were performed with Cavity Ring-Down Spectroscopy gas measurement device Picarro G2508. When performing emission measurements, the flow rate in the tube of the mesurement device was 5 ml/s-1. During the whole experiment, water-saturated compost was used as a source of methane and nitrous oxide. The experimental structure also used sand, which served as a gas-permeable layer and separated biocover from compost (emissions source). The analysis of the data showed a minor interaction between methane emissions and nitrous oxide flow. The results shed light on potential synergies or tradeoffs in greenhouse gas reduction strategies, offering valuable insights for optimizing emission reduction efforts while considering the broader environmental implications. [ABSTRACT FROM AUTHOR]

Drosophila as a model for toxicological study is a simple and effective test-system owing to (i) availability of a wide range of accessible methods allowing the integral assessment of the negative effect of the factor under study on the vital characteristics at all levels of the organism vital activity (from molecular to population), (ii) possibility of obtaining results within a relatively short period of time (from 2 days to 2 weeks); (iii) low price of working methods, (iv) absence of specific requirements as to work with an object. The aim of the work was to assess the air quality in the city of Kazan based on the change of the drosophila's vital parameters: the survival rate at the stage of embryogenesis, the fertility (the number of eggs), the locomotion, the degree of the intestines tissue damage and the level of DNA-comets. To analyze the air quality, we selected such city zones as, Privolzhsky (1), Moskovsky (2), Sovetsky (3). It was shown that flies that were in zones 1 and 2, but not in 3, are characterized by a decrease in fertility, an increase in embryonic death of offspring, intestines tissue damage and DNA damage. [ABSTRACT FROM AUTHOR]

Air pollution is a serious issue in most parts of Bihar, especially in its capital city, Patna. The air quality in Patna has significantly worsened due to factors like rapid urbanization, increased traffic, and various natural and human-related causes. This decline in air quality has led to several negative health effects. In light of this, the aim of this study was to examine how air pollution affects the long-term health of Patna's residents, taking into account age and exposure time as important factors. We gathered data from one busy intersection in Patna, specifically Danapur. Health effects from air pollution were collected from the residents via a formatted questionnaire. To analyze the relationship between age, exposure time, and the health effects reported by the participants, we used a statistical test called the chi square test of independence. The findings of the study revealed a clear link between age, exposure time, and the health status of the participants. We concluded that older individuals and those with longer exposure times faced a higher risk associated with the increasing air pollution levels. This study provides a foundation for raising awareness among both authorities and the general public of the adverse health impacts associated with declining air quality, emphasizing the urgency in taking appropriate measures to counter this challenge. [ABSTRACT FROM AUTHOR]

We examine several different features of DSDs based on data and observations from two mid-latitude coastal locations: (a) the Delmarva peninsula, USA, and (b) Incheon, South Korea. In each case, the full DSD spectra were obtained from two collocated disdrometers. Two events from location (a) and one event from location (b) are presented. For (a), observations and retrievals from NASA's S-band polarimetric radar are included in the analyses as well as retrieved DSD parameters from the dual-wavelength precipitation radar onboard the Global Precipitation Measurement satellite. For (b), the disdrometer-based DSD data are compared with measurements from another sensor. Our main aim is to examine the underlying shape of the DSDs and their representation by the generalized gamma model. [ABSTRACT FROM AUTHOR]

Delhi is one of the most densely populated megacities in the world, and it is experiencing deteriorating air quality due to rapid industrialization and excessive use of transportation. The limited emission control measures in Delhi have led to worsening air quality problems, which have become a serious threat to human health and the environment. In the present study, we investigate the long-term (2011–2021) interrelationship between air pollutants and the vegetation index using satellite datasets. Air pollutant data viz. nitrogen dioxide (NO2) and sulfur dioxide (SO2) were obtained from NASA's Aura satellite called the Ozone Monitoring Instrument (OMI)Additionally, the data for carbon monoxide (CO) and particulate matter 2.5 (PM2.5) were obtained from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) model. The vegetation indices, i.e., the normalized difference vegetation index (NDVI) and the enhanced vegetation oxide (EVI), were collected from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The analysis of both datasets revealed higher concentrations of air pollutants in the summer months when the NDVI and EVI were minimal. Furthermore, a higher pollution load was observed in the months of October–January when the NDVI and EVI were lower. Furthermore, we also investigated the spatial patterns of PM2.5 and other gaseous pollutants (viz. CO, SO2, and NO2) and observed that their levels were lower in the vegetated region in comparison to the sparsely vegetated area of Delhi. The present study indicates that vegetation could ameliorate various air pollutants; however, it needs to be validated with ground observed data. [ABSTRACT FROM AUTHOR]

The increased concentration of pollutants is a challenge to the health of the population. This work aims to investigate the health risk that is related to the pollutants' level in the center of Rhodes city. Rhodes Island is a desirable tourist destination with important economic activity over the southeastern Aegean Sea. This analysis covers the (summer) July–August months and the (winter) December month of 2022. Hourly recordings of the concentrations of PM2.5, NO2 and O3 from a mobile air quality monitoring system (AQMS) are analyzed. In order to investigate the effects of pollution level on people's health, the Air Quality Health Index (AQHI) is calculated. Results show that summer shows an increased health danger compared to winter period, possibly due to increased traffic emissions, tourist density and the different meteorological conditions. In the summer period, the AQHI is classified between the middle and upper-medium health risk class. During the winter month, AQHI is mainly classified in the low-medium health risk class. The summer shows increased health risk despite the AQHI diurnal variability being lower when compared to December. Additionally, the diurnal differences between the two periods show an increased health risk in the summer period for the majority of the hours. Finally, this analysis shows that traffic activities possibly affect the health risk and also highlights that the authorities should adopt green policies to protect human health and the environment. [ABSTRACT FROM AUTHOR]

The air quality index is an important air quality standard for assessing air pollution, wherein to develop spatio-temporal air quality index prediction models in hourly temporal frequencies using ground-based sensor data combined with satellite meteorological and remote sensing data. Applying machine learning method, mainly encoder decoder long short term memory (EDLSTM) as the predictor model, this paper describes daily prediction studies of air quality index (AQI) level and particulate matter (PM)2.5 concentration. In the process, the data preprocessing and exploratory data analysis (EDA) approach was used to mitigate data processing errors by identifying missing values, outliers, and features correlation in the whole dataset. It then was continued by analyzing trends of identified pollutants over time to develop models that produce future predictive outputs. The final work showed that for the AQI standard, EDLSTM model with the root mean squared propagation (RMSProp) optimizer at epoch 50 is the best prediction model since it produced the lowest root mean squared error (RMSE) and mean absolute error (MAE) values, 5.96 and 8.09 sequentially. Meanwhile, the model with Adam optimizer at epoch 700 is the best prediction for PM2.5 concentration standard, in which the RMSE and MAE delivered the lowest values, 2.53 and 3.53. [ABSTRACT FROM AUTHOR]

The people's quality of life is affected by ambient air quality. In Ukraine, the confectionery enterprises are a significant emission source of organic PM10. The organic PM10 is a pathogen, an allergen and indirect emission source of CO2. There is a need to determine the contribution organic PM10 from confectionery enterprise in the surface air on the outside of confectionery enterprise sanitary protection area. In this study, PM10 concentration was determined through the direct measurements method. The measurement in the winter season were performed in a residential area near the sanitary protection area of the enterprise. The mean value of PM10 at all direct measurement sites was 78.0 µg/m3 (standard deviation, SD: 6.1 µg/m3; 95 % confidence interval, CI: 74.2–81.8 µg/m3). PM10 in ground air outside the sanitary protection zone near apartment buildings in the winter period outweighs daily means limit values of PM10 for the protection of human health applied in EU by a factor of 1.6. PM10 particles by 89.8 % consist of the most environmentally dangerous particles with a size of up to 2.5 µm. Apply ionization treatment of cocoa dust emissions to increase the efficiency of collecting PM10 is proposed. [ABSTRACT FROM AUTHOR]

Our daily activities and quality of life are impacted by air pollution. It threatens the Earth's ecosystems and quality of life, as well as human health. The increased industrial activity in recent years has highlighted the urgent necessity to monitor air quality. People must understand how their actions effect air quality. An air quality monitoring system is proposed in the proposal. To monitor air quality, the system was created utilizing an Arduino microcontroller and various sensors. The air quality monitoring system is designed to monitor and evaluate real-time air quality, save data to a remote server, and update data through the Internet. Parts per million (PPM) is the unit of measurement for air quality. The developed system's air quality measurements are accurate. The results are exhibited on the design hardware's display surface and may be accessed from any smart mobile device via the cloud. [ABSTRACT FROM AUTHOR]

The development of the country needs economic support, and a large part of the source of the economy comes from the industrial zone. As the industrial zone grows, it can attract foreign investors to bring up the country's economy. However, inevitably, the air pollutants emitted by the industrial zone also increase with the growth of the industrial zone, and if the concentration of these air pollutants exceeds a certain value in the atmosphere, it will damage human health. Therefore, the objectives of this paper focus on identifying the concentration of the major air pollutants emitted such as particulate matter (PM10), sulphur dioxide (SO2), nitrogen oxide (NOx) and carbon monoxide (CO) and overlay the hotspot thematic map on residential area. These objectives will be achieved by using Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) to simulate the air quality of the industrial park throughout the Klang Valley. Then, by comparing the average concentration for each pollutant obtained from the HYSPLIT model with the World Health Organization (WHO) Air Quality Guidelines (AQG) and Recommended Malaysia Air Ambient Quality Guideline (RMAAQG) standards to assess the potential health impacts to the sensitive receptors. In addition, comparing the hourly concentration with the analysis from previous years for benchmarking to increase the accuracy of the results in this paper. [ABSTRACT FROM AUTHOR]

Haze is defined as the collection of particulate matter at high levels in a segment of the atmosphere producing milky weather and reducing visibility. Sectors of societies are heavily impacted during a haze episode, mainly due to the chemicals associated with haze. The goal of this thesis is to use the HYSPLIT simulation model simulate a backward trajectory for three locations across Malaysia namely Cheras, Pasir Gudang and Miri, to track the origin of the source while identifying other possible contributors to the decline in air quality. In Malaysia during September 2019, the annual unchecked peatland deforestation in Indonesia is the leading cause of haze. In Indonesia during 2019 the peatland fire, emitted smoke at a rate of 0.105 million tons per kilogram. Largest source of local pollution emissions was determined to be motor vehicles, which release about 271.6872 kilograms per hour. For verification purposes, a comparison of the satellite observations provided by the Association of Southeast Asian Nations Metrological Specialized Center with the results obtained from HYSPLIT backward trajectories is made to justify the simulation settings. [ABSTRACT FROM AUTHOR]