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Journal articles on the topic 'Forecasting environmental pollution'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

CHERENKEVYCH, O. "Projections of Environmental Pollution in Ukraine." Scientific Bulletin of the National Academy of Statistics, Accounting and Audit, no. 3 (December 22, 2020): 28–37. http://dx.doi.org/10.31767/nasoa.3-2020.03.

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The balanced environmentally sustainable economic development of Ukraine can be achieved by increasing the efficiency of the nature protection efforts, which requires the improved methods for assessment, modeling and forecasting of environmental performance and environmental pollution indicators. The article’s objective is to make a statistical forecasting of environmental pollution indicators in Ukraine, to outline the areas of nature protection work in this country. Air pollution, water pollution and hazardous wastes in Ukraine are forecasted considering the main factors of influence, select

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Olascoaga, M. J., and G. Haller. "Forecasting sudden changes in environmental pollution patterns." Proceedings of the National Academy of Sciences 109, no. 13 (2012): 4738–43. http://dx.doi.org/10.1073/pnas.1118574109.

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He, Yuanfang. "Development of a trend forecasting model for environmental pollution monitoring." Management of Development of Complex Systems, no. 57 (March 29, 2024): 62–66. http://dx.doi.org/10.32347/2412-9933.2024.57.62-66.

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A complex model for forecasting time series of environmental pollution indicators is described, considering the aggregation of various forecasting models, which are formed based on predictive statistical analysis of pollution indicators and have an adaptive nature. The model differs from known models by providing the possibility of adapting the model parameters to changes in the state of the environment, which is especially important in the conditions of using such models in monitoring systems. Th e complex forecasting model includes higher-order exponential smoothing, Holt, Winters, moving av

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Alwan, Asraa, shasho T., Waleed Rodeen, and Hindreen Tahir. "Forecasting the Impact of Waste on Environmental Pollution." International Journal of Scientific Research and Sustainable Development 1, no. 1 (2018): 1–12. http://dx.doi.org/10.21608/ijsrsd.2018.5142.

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Radaev, Anton, and Elena Korneeva. "Method for forecasting pollution of urban areas." E3S Web of Conferences 140 (2019): 09005. http://dx.doi.org/10.1051/e3sconf/201914009005.

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A model for substantiating the parameters of regression models for a comprehensive indicator of environmental pollution has been developed. A distinctive feature is the separate consideration of the influence of factors of the natural and industrial environment, as well as the linear nature of the interaction of nonlinear variables. The resulting model will allow us to analyze the current state of the environment depending on the quantity and quality of environmental indicators, and also identify critical changes in it. In the urban development industry, this model will help in planning the po

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Majeed, Dilovan Asaad, Hawar Bahzad Ahmad, Ahmed Alaa Hani, et al. "DATA ANALYSIS AND MACHINE LEARNING APPLICATIONS IN ENVIRONMENTAL MANAGEMENT." Jurnal Ilmiah Ilmu Terapan Universitas Jambi 8, no. 2 (2024): 398–408. http://dx.doi.org/10.22437/jiituj.v8i2.32769.

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The rapid expansion of data on air contaminants and climate change, particularly concerning public health, presents both opportunities and challenges for traditional epidemiological methods. This study aims to address these challenges by exploring advanced data collection, pattern identification, and predictive modeling techniques in the context of air pollution research. The focus is leveraging data mining and computational methods to enhance the understanding of air pollution's impact on public health, specifically ozone exposure. A comprehensive review of the scientific literature was condu

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Domańska, D., and M. Wojtylak. "Explorative forecasting of air pollution." Atmospheric Environment 92 (August 2014): 19–30. http://dx.doi.org/10.1016/j.atmosenv.2014.03.041.

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8

Basok, Boris. "The energy sector and environmental pollution." Visnik Nacional'noi' academii' nauk Ukrai'ni, no. 3 (March 2022): 30–36. http://dx.doi.org/10.15407/visn2022.03.030.

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The report examines the historical and forecasting aspects of global warming, the structure and dynamics of greenhouse gas emissions and emissions of hazardous gases in Ukraine. The main provisions of the Strategy for Environmental Safety and Adaptation to Climate Change until 2030, as well as the National Plan for Reducing Emissions from Large Combustion Plants until 2033 are analyzed. Data on the dynamics of primary and final energy use, on the amount of global investments in energy efficiency, as well as data on energy efficiency of primary energy resources and the degree of carbon content

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Guruprasath.I, Vasanth.R, Vishnuvarthan.S, Deglus Jovin, V. Gopika., and M. Kirubadevi. "Air Pollution Forecasting using Data Mining Technique." International Journal of Innovative Science and Research Technology 7, no. 2 (2022): 418–22. https://doi.org/10.5281/zenodo.6331287.

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Air pollution is one of the foremost hazards of environmental pollution. None of the living effects will survive while not having similar air. still, as a result of buses, agrarian conditioning, manufactories and diligence, mining conditioning, burning of fossil energies our air is carrying impure. This conditioning unfolds contaminant, gas, monoxide, particulate adulterants in our air that are dangerous for all living organisms. The air we tend to breathe each moment causes numerous health problems. thus, we want an honest system that predicts similar profanations and is useful in an advanced

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Bashirov, M. G., R. G. Vildanov, A. M. Khafizov, A. S. Khismatullin, and D. S. Akchurin. "Forecasting Prevention of Air Pollution Using Aan Intelligent Environmental System." Ecology and Industry of Russia 28, no. 1 (2024): 16–21. http://dx.doi.org/10.18412/1816-0395-2024-1-16-21.

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The article deals with the actual problem of atmospheric air pollution with technogenic chemicals that negatively affect the ecology of the Salavat air basin of the Republic of Bashkortostan. An intelligent environmental monitoring system is proposed, which is able to give an integral assessment of the state of the city's air basin, identify sources of increased air pollution, process information using an artificial neural network in online mode, and also develop recommendations for enterprises to optimize their operating mode.

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Subramaniam, Shankar, Naveenkumar Raju, Abbas Ganesan, et al. "Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review." Sustainability 14, no. 16 (2022): 9951. http://dx.doi.org/10.3390/su14169951.

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Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid

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Biliaieva, V. V., I. V. Kalashnikov, O. V. Berlov, V. A. Kozachyna, and O. A. Tymoshenko. "COMPLEX ASSESSMENT OF POLLUTION ZONES IN EXTREME SITUATIONS AT ENERGY FACILITIES." Ukrainian Journal of Civil Engineering and Architecture, no. 6 (018) (February 24, 2024): 22–28. http://dx.doi.org/10.30838/j.bpsacea.2312.261223.22.1003.

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Problem statement. The task of forecasting damage zones under various types of man-made load in the event of an extreme situation at a gas station is considered. Comprehensive forecasting includes the determination of areas of chemical, thermal and mechanical contamination in the event of an extreme situation at a gas station. The purpose of the article. Development of a theoretical toolkit and a package of programs for complex forecasting of chemical, thermal and chemical environmental pollution zones in the event of an extreme situation at a gas station. Methodology. Forecasting of zones of

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Grafkina, M. V., and A. V. Pitryuk. "Improvement of the soil pollution monitoring system." IOP Conference Series: Earth and Environmental Science 1010, no. 1 (2022): 012087. http://dx.doi.org/10.1088/1755-1315/1010/1/012087.

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Abstract The Russian Federation has established a fairly effective system of environmental monitoring, including soil pollution with industrial toxicants and pesticides. The results of environmental monitoring are included in stock materials reflecting the state of the environment. First of all, they include the yearbooks of the Federal Service for Hydrometeorology and Environmental Monitoring of the Russian Federation. Environmental monitoring data make it possible to identify the level of soil pollution in various regions, formulate recommendations on the state of soil pollution and the poss

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Blinayeva, Y., S. Smailova, A. Aulbekov, and Y. Yaanus. "APPLICATION OF NEURAL NETWORKS FOR ATMOSPHERIC POLLUTION FORECASTING." BULLETIN of D. Serikbayev EKTU 3 (September 30, 2023): 99–113. http://dx.doi.org/10.51885/1561-4212_2023_3_99.

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A large number of hazardous emissions from industrial production is an environmental problem for the world cities. In the field of environmental engineering, the study of air quality and the prediction of changes in concentrations of harmful substances will make it possible to develop the right strategies for sustainable development. The paper presents the results of the development and research of the applicability of neural network modeling models for forecasting and distribution of concentrations of emissions into the atmosphere by the example of Ust-Kamenogorsk, Kazakhstan. For forecasting

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Böhm, Gisela, and Hans-Rüdiger Pfister. "Anticipated and experienced emotions in environmental risk perception." Judgment and Decision Making 3, no. 1 (2008): 73–86. http://dx.doi.org/10.1017/s1930297500000188.

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AbstractAffective forecasting with respect to two environmental risks (ozone depletion, air pollution) was investigated by studying tourists who travelled to either Australia or Bangkok and were thus confronted with one of these risks. We measured anticipated outcome and anticipated emotions before the journey, actually experienced outcome and actually experienced emotions during the journey, and anticipated outcome and emotions concerning a future encounter with the same risk after the journey. Results indicate that tourists underestimate (air pollution) or correctly predict (ozone depletion)

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Side, Jonathan. "Delphi 2000—Forecasting environmental issues and controls." Marine Pollution Bulletin 17, no. 11 (1986): 487–88. http://dx.doi.org/10.1016/0025-326x(86)90634-x.

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Suresh, S., M. R. Sindhumol, M. Ramadurai, D. Kalvinithi, and M. Sangeetha. "Forecasting Particulate Matter Emissions Using Time Series Models." Nature Environment and Pollution Technology 22, no. 1 (2023): 221–28. http://dx.doi.org/10.46488/nept.2023.v22i01.020.

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Environmental pollution is a serious concern nowadays with its disastrous impact on living organisms. In several types of pollution, Air pollution takes on a crucial role by directly affecting the respiratory system and causing fatal diseases in humans. Air pollution is a mixture of gaseous and particulate matter interweaved by different sources and emanating into the atmosphere. In particular, particle pollutants are critical in growing air pollution in India’s main cities. Forecasting the particulate matter could mitigate the complications caused by it. The employment of a model to predict f

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Kumar Gupta, Anuj. "Study of different Air Quality Forecasting Models." CGC International Journal of Contemporary Technology and Research 6, no. 2 (2024): 396–99. http://dx.doi.org/10.46860/cgcijctr.2024.06.10.396.

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One of the main effects of urban growth is industrialization, which adds to the strain on resources like water and the environment. In recent years, air pollution has emerged as one of the biggest environmental challenges in emerging nations. It has negative impacts on people, plants, and animals. According to a recent study, air pollution is responsible for 3.3 million premature deaths worldwide each year. Therefore, air quality forecasting is crucial since it lessens the negative effects of air pollution on human health. In this paper, the various models used to predict air quality have. Air

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Roy, Richa. "Clear Skies: Air Quality Forecasting." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50832.

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- Air Quality Forecasting uses real-time and historical data to predict future air pollution levels. It helps assess pollutant concentrations like PM2.5, PM10, NO2, SO2, CO, and O3. The system predicts air quality using GRU, LSTM, and LR models with real-time data from public sources. It provides location-based recommendations: health advice for sensitive groups and actionable measures for authorities, like traffic restrictions. An interactive chatbot answers queries, predicts AQI, offers health guidance, and suggests preventive measures. A user-friendly interface displays real-time air qualit

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Achiriloaiei, Daniel, and Mihai Medeleanu. "Studies on the Effects of Environmental Pollution on ACSR Conductors." Revista de Chimie 70, no. 11 (2019): 3984–86. http://dx.doi.org/10.37358/rc.19.11.7687.

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Degradation and breakage of Aluminium Clad Steel Reinforced (ACSR) conductors leads to power outages that have catastrophic impact from an economic, social and national security point of view. Frictions, vibrations, corrosion, as well as other chemical compounds from the environment, can be the main source of the damages. Environment provides much of the time less restrictive conditions than those designed in the standards. In this respect, issues of optimization and development of current diagnostics and forecasting techniques personalized on geographic areas arise.

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Nguyen, Phuoc Van, Hien Thi Thu Nguyen, Truong Duy Cao, and Lam Minh Nguyen. "FORECASTING ENVIRONMENT POLLUTION LEVELS BY INDUSTRIAL DEVELOPMENT ARISING TO 2020 IN LONG AN PROVINCE." Science and Technology Development Journal 14, no. 1 (2011): 29–44. http://dx.doi.org/10.32508/stdj.v14i1.1865.

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In this article, the results of predicted environmental pollution load of air, water and solid waste by the operation of the park / industrial zones in Long An province by 2020 are presented. Two mathematic model, ISC3 and Mike 11, were used to determine the spread of environmental pollution of air and water. The results showed that, to make the air environment quality meet QCVN standard, we need to cut gas emission to 57% by 2015 and 80% by 2020. For water environment, the forecast maps on spread of water pollution can help to identify the source that cause water pollution.

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Lomazov, P., A. Pavlуchenko, and Yu Buchavyi. "Methods for forecasting air pollution based on machine learning." Collection of Research Papers of the National Mining University 79 (December 30, 2024): 278–91. https://doi.org/10.33271/crpnmu/79.278.

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Goal. To investigate modern methods for predicting air pollution using machine learning algorithms. Methodology. A modeling approach was used to develop forecasting algorithms that consider the temporal and spatial characteristics of the data. The method of specification was applied to refine the dependencies between variables. Statistical generalization methods were also employed to remove noise, fill in missing values, and identify trends and anomalies. Additionally, this study examines the Random Forest, LSTM, GBM, SVM, and MLP algorithms, discussing their advantages, limitations, and poten

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Chertes, K. L., A. A. Bukin, N. S. Bukhman, V. N. Pystin, and O. V. Tupitsyna. "Forecasting and Elimination of Pollution Generated by Objects of Accumulated Environmental Damage (on the Example of Inactive Sludge Reservoirs)." Ecology and Industry of Russia 27, no. 9 (2023): 33–39. http://dx.doi.org/10.18412/1816-0395-2023-9-33-39.

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The forecasting and pollution remediation principles of decommissioned sludge reservoirs were proposed. Forecasting aspects based on the model of mass transfer of pollutants in the subsurface from the standpoint of the laws of filtration theory were studied. The results of an industrial trial on contaminated subsurface flush on one of the storage tanks for extremely hazardous waste in the Samara region were presented.

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Ramirez-Velarde, Raul, Oscar Esquivel-Flores, and Gerardo Mejía-Velázquez. "Forecasting Air Pollution Contingencies Using Predictive Analytic Techniques." Atmosphere 15, no. 11 (2024): 1271. http://dx.doi.org/10.3390/atmos15111271.

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The proliferation of pollutants affects the world’s population, mainly those who live in large cities. Neurological and cardiovascular dysfunctions have a correlation with air particulate matter concentration, among other chronic diseases. Therefore, it is important to utilize different methods of analysis to build predictive models that can identify possible concentrations of contaminants in metropolitan areas. This work presents a methodology that will enable the forecasting of severe pollution contingencies using weather measurements as input variables. This predictive analytical technique

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Zhang, Yuyi, Qiushi Sun, Jing Liu, and Ovanes Petrosian. "Long-Term Forecasting of Air Pollution Particulate Matter (PM2.5) and Analysis of Influencing Factors." Sustainability 16, no. 1 (2023): 19. http://dx.doi.org/10.3390/su16010019.

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Long-term forecasting and analysis of PM2.5, a significant air pollution source, is vital for environmental governance and sustainable development. We evaluated 10 machine learning and deep learning models using PM2.5 concentration data along with environmental variables. Employing explainable AI (XAI) technology facilitated explainability and formed the basis for factor analysis. At a 30-day forecasting horizon, ensemble learning surpassed deep learning in performance, with CatBoost emerging as the top-performing model. For forecasting horizons of 90 and 180 days, Bi-SLTM and Bi-GRU, respecti

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Sharshenova, Damira. "MAJOR ENVIRONMENTAL PROBLEMS AND ENVIRONMENTAL IMPACT OF THE KAVAK LIGNITE BASIN’S OPEN MINING." Alatoo Academic Studies 21, no. 3 (2021): 308–15. http://dx.doi.org/10.17015/aas.2021.213.033.

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The article presents an overview of the coal industry in the Kyrgyz Republic, environmental risks and consequences in current coal mining on the example of the Kavak lignite basin, as well as theoretical aspects of the study of the mineral resources of this coalfield. The impact of coal mining on air pollution and hydrosphere, and soil disturbance are studied. Moreover, environmental protection measures are reviewed for coal mining, which should be carried out in an integrated manner, i.e. they should jointly reduce the negative impact on the environment, and the implementation of these measur

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Liao, Kuo, Xiaohui Huang, Haofei Dang, Yin Ren, Shudi Zuo, and Chensong Duan. "Statistical Approaches for Forecasting Primary Air Pollutants: A Review." Atmosphere 12, no. 6 (2021): 686. http://dx.doi.org/10.3390/atmos12060686.

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Air pollutant forecasting can be used to quantitatively estimate pollutant reduction trends. Combining bibliometrics with the evolutionary tree and Markov chain methods can achieve a superior quantitative analysis of research hotspots and trends. In this work, we adopted a bibliometric method to review the research status of statistical prediction methods for air pollution, used evolutionary trees to analyze the development trend of such research, and applied the Markov chain to predict future research trends for major air pollutants. The results indicate that papers mainly focused on the effe

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Chelani, Asha B. "Nearest Neighbour Based Forecast Model for PM10 Forecasting: Individual and Combination Forecasting." Aerosol and Air Quality Research 15, no. 3 (2015): 1130–36. http://dx.doi.org/10.4209/aaqr.2014.09.0229.

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Biliaieva, V. V., O. I. Gubin, P. B. Mashykhina, A. Yu Usenko, and L. O. Tymoshenko. "ASSESSMENT OF THE ENERGY FACILITY IMPACT ON ENVIRONMENTAL POLLUTION." Ukrainian Journal of Civil Engineering and Architecture, no. 6 (018) (February 24, 2024): 16–21. http://dx.doi.org/10.30838/j.bpsacea.2312.261223.16.1002.

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Problem statement. The task of assessing the influence of the boiler house on atmospheric air pollution is under consideration. Carrying out such an assessment is very important when locating boiler houses near residential areas. Since the weather conditions for each region are characterized by a change in the direction of the wind, the intensity of atmospheric diffusion, the appearance of a calm, it is important to have information about the formation of pollution zones in sedimentary zones. To solve this important problem, it is very important to use mathematical modeling, since it is curren

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Moreno-Vazquez, Francisco-Javier, Felipe Trujillo-Romero, and Amanda Enriqueta Violante Gavira. "Optimizing Air Pollution Forecasting Across Temporal Scales: A Case Study in Salamanca, Mexico." Earth 6, no. 1 (2025): 9. https://doi.org/10.3390/earth6010009.

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Air pollution forecasting is essential for understanding environmental patterns and mitigating health risks, especially in urban areas. This study investigates the forecasting of criterion pollutants—CO,O3,SO2,NO2,PM2.5, and PM10—across multiple temporal frames (hourly, daily, weekly, monthly) in Salamanca, Mexico, utilizing temporal, meteorological, and pollutant data from local monitoring stations. The primary objective is to identify robust models capable of short- and mid-term predictions, despite challenges related to data inconsistencies and missing values. Leveraging the low-code PyCare

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Rashmitha, Shamitha N Shetty, and Neema H. "MACHINE LEARNING-BASED FORECASTING OF AIR QUALITY INDEX." International Journal of Engineering Applied Sciences and Technology 09, no. 01 (2024): 197–201. http://dx.doi.org/10.33564/ijeast.2024.v09i01.032.

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Air pollution increases the risk of various illnesses that harm people. Therefore, it is important to establish fore casting methods for authorities. This research developed a solid ML models for predicting the Air Quality Index (AQI) using a Random Forest Regressor. By leveraging a comprehensive dataset from the Central Pollution Control Board (CPCB), this comprises information from major Indian cities like Bangalore, Mangalore, Delhi, Ahmedabad, Mumbai, and Chennai, the model successfully identified key pollutants, PM10 and PM2.5, as significant predictors of AQI. The findings highlight the

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Komarudin, Muhamad, Sri Ratna Sulistiyanti, Suharso Suharso, et al. "Advancing precision in air quality forecasting through machine learning integration." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 3 (2025): 2113. https://doi.org/10.11591/ijai.v14.i3.pp2113-2122.

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In an era where environmental concerns are escalating, air quality forecasting emerges. Forecasting is a crucial tool for addressing the adverse impacts of pollution on public health and ecosystems. In urban centers like Bandar Lampung, economic activities intensify pollution levels. This condition leveraging advanced machine learning forecasting methods can significantly mitigate these effects. This study evaluates the precision of long short-term memory (LSTM) and Prophet methods in predicting air quality. This study utilizes data from January 12, 2022 to November 9, 2023. The results reveal

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Vadurin, Kyrylo, Andrii Perekrest, Volodymyr Bakharev, Vira Shendryk, Yuliia Parfenenko, and Sergii Shendryk. "Towards Digitalization for Air Pollution Detection: Forecasting Information System of the Environmental Monitoring." Sustainability 17, no. 9 (2025): 3760. https://doi.org/10.3390/su17093760.

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This study addresses the urgent need for advanced digitalization tools in air pollution detection, particularly within resource-constrained municipal settings like those in Ukraine, aligning with directives such as the AAQD. The forecasting information system for integrating data processing, analysis, and visualization to improve environmental monitoring practices is described in this article. The system utilizes machine learning models (ARIMA and BATS) for time series forecasting, automatically selecting the optimal model based on accuracy metrics. Spatial analysis employing inverse distance

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Yang, Rui. "Time Series Analysis: Application of LSTM model in predicting PM 2.5 concentration in Beijing." ITM Web of Conferences 70 (2025): 04022. https://doi.org/10.1051/itmconf/20257004022.

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Air pollution forecasting for public health and policy-making has a critical importance, this paper employs a Long Short-Term Memory (LSTM) model to perform in-depth prediction of PM2.5 concentrations measured at the U.S. Embassy in Beijing, outperforming regular forecasting approaches. In the LSTM model, the research examines a very detailed hourly dataset and beats regular forecasting approaches. A key finding is the model’s ability to effectively generalize from historical data to predict future air quality trends, with its adeptness at handling time-dependent relationships. This research e

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Peters, Heinz-Joachim. "The Significance of Environmental Precaution in the Environmental Impact Assessment Directive." European Energy and Environmental Law Review 5, Issue 7 (1996): 210–13. http://dx.doi.org/10.54648/eelr1996036.

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The cornerstone of the law on the assessment of the effects of certain public and private projects on the environment is the prevention of pollution. This principle is expressed in three different ways. Firstly, the term "interaction" between factors as an expression of the ecosystem in Article 3 means that protection has shifted from the factors to the system. Secondly, the term "impact" includes nuisances so that the environment must be protected even when damage does not occur. Finally, low probabilities are sufficient when forecasting impacts. This kind of prevention is the standard to be

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Trong Diep, Nguyen, Nguyen Anh Thu, and Pham Hung Nhan. "Further Discussion on Implementing Environmental and Law on Environmental Protection 2020." Mediterranean Journal of Basic and Applied Sciences 07, no. 02 (2023): 10–15. http://dx.doi.org/10.46382/mjbas.2023.7202.

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Environmental protection is activities that keep the environment clean and beautiful. Improve the environment, ensure ecological balance. Preventing and overcoming bad consequences caused by humans and nature to the environment. Exploit, rationally and economically use natural resources. Environmental protection activities must be conducted regularly, openly and transparently; Prioritize forecasting, prevention of pollution, incidents, environmental degradation, management of environmental risks, reduction of generation and waste, increased reuse and recycling of waste to exploit the value of

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Wang, Yiwei. "Pollutant Significance Assessment and AQI Forecasting Framework: Based on Linear Regression and xLSTM." Theoretical and Natural Science 125, no. 1 (2025): 73–79. https://doi.org/10.54254/2753-8818/2025.gl25429.

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Air pollution has become a global environmental problem worth considering, so accurate prediction and targeted plans for air pollution are growing increasingly important. This study aims to predict the Air Quality Index (AQI) using extended Long Short-Term Memory (xLSTM) on an air quality dataset spanning 5 years, leveraging its enhanced sequence modeling capabilities., in order to provide a scientific basis for environmental policy formulation and public health intervention. Also, linear regression was used to study which specific air pollutants have the greatest impact on AQI. This enables t

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Abrol, Raghav. "AI-Powered Anomaly Detection in Air Pollution for Smart Environmental Monitoring." Indian Journal of Artificial Intelligence and Neural Networking 5, no. 3 (2025): 1–5. https://doi.org/10.54105/ijainn.c1098.05030425.

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Air pollution is a growing concern due to its adverse effects on human health and the environment. Traditional air quality monitoring stations provide accurate data but are expensive to maintain and limited in coverage. This research explores an AI-based anomaly detection framework to enhance air quality assessment and support the development of virtual monitoring stations. The study utilizes four machine learning techniques—Z-score, Isolation Forest, Autoencoders, and Long Short-Term Memory (LSTM) networks—to analyse pollution data. The Z-score method detects extreme pollution values by measu

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Gordon, Vladimir, Olga Pilipenko, and Tatiana Poturaeva. "Assessment of the Impact of the Motor Transport Complex on the Ecological and Demographic Situation in the Region." MATEC Web of Conferences 334 (2021): 01026. http://dx.doi.org/10.1051/matecconf/202133401026.

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The aim of this work is to build a mathematical model of the dynamics and mutual influence of the population and environmental factors – pollution of the atmosphere and surface water bodies – on an urbanized territory. The paper proposes a simulation model of dynamics and interaction of external and internal directions in biosphere-compatible city activities. The external direction of city’s activities is removal of resources from the Biosphere and emission of waste polluting water, air and soil into it. The internal direction is the impact on the population: environmental state, health assess

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Cui, Xiwen, Shaojun E, Dongxiao Niu, Dongyu Wang, and Mingyu Li. "An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target." Sustainability 13, no. 15 (2021): 8670. http://dx.doi.org/10.3390/su13158670.

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In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The

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Shafii, Nor Hayati Binti, Rohana Alias, Nur Fithrinnissaa Zamani, and Nur Fatihah Fauzi. "Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)." Journal of Computing Research and Innovation 5, no. 3 (2020): 43–53. http://dx.doi.org/10.24191/jcrinn.v5i3.149.

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Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development. Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health. The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Sup

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Shin, Yeeun, Suyeon Kim, Jinsil Park, Sang-Woo Lee, and Kyungjin An. "Effectiveness of Particulate Matter Forecasting and Warning Systems within Urban Areas." Sustainability 14, no. 9 (2022): 5394. http://dx.doi.org/10.3390/su14095394.

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The close relation between atmospheric pollution and human health has been well documented. Accordingly, various policies have been enacted worldwide to reduce and regulate air pollution, with most countries having established correlated monitoring systems. Notably in South Korea, increasing concerns about particulate matter (PM) concentrations led to the establishment of a nationwide forecasting and warning system in 2014. In this study, the PM trends in South Korea over the past decade were examined, and the correlated social issues were analyzed. In addition, the relationships between PM co

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Mahadik, Sakshi. "Air Quality Forecasting Using Deep Learning Framework." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6578–83. http://dx.doi.org/10.22214/ijraset.2023.53176.

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Abstract: Air quality is a crucial environmental factor that affects human health and well-being. Accurate forecasting of air quality can aid in mitigating the adverse effects of air pollution. In this research paper, we propose a deep learning-based approach for air quality forecasting. We utilize Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BILSTM) models to capture spatial and temporal dependencies in air quality data. The existing system is analyzed, and its limitations are identified. Our proposed solution overcomes these limitations by incorporating deep

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Das, Shuvendu, Karanvir Singh, and Kiranjeet Kaur. "Air Quality Prediction in Beijing: Machine and Deep Learning Analysis." ITM Web of Conferences 68 (2024): 01012. https://doi.org/10.1051/itmconf/20246801012.

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In densely populated urban hubs like Beijing, the presence of PM2.5, a critical air quality metric, poses significant hazards to human health and the environment. This study delves into predictive modeling approaches for forecasting PM2.5 concentrations in response to escalating concerns.We analyze a wide range of approaches, including RDF, CNN, STM and fundamental statistical techniques, by closely analyzing Beijing’s PM2.5 concentrations and historical meteorological information. According to our research, CNN outperforms LSTM and shows excellent accuracy in forecasting PM2.5 levels. While r

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ГАЛАКТІОНОВ, МИКОЛА, та ВІКТОР БРЕДУН. "ІНТЕГРАЦІЯ МОДЕЛЕЙ ПРОГНОЗУВАННЯ ДЛЯ ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ МОНІТОРИНГУ ТРАНСПОРТНОГО ВПЛИВУ НА ДОВКІЛЛЯ". Herald of Khmelnytskyi National University. Technical sciences 343, № 6(1) (2024): 68–72. https://doi.org/10.31891/2307-5732-2024-343-6-9.

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Urban air pollution caused by vehicle emissions is becoming a growing problem, especially in industrially loaded cities such as Kryvyi Rih. Constant heavy traffic and the operation of vehicles that do not meet modern environmental standards significantly affect the quality of atmospheric air. Traditional methods of pollution control are often insufficient to respond in a timely manner to continuous changes in air quality, resulting in the introduction of restrictions only after critical levels of pollution have been reached. This approach is ineffective for long-term air quality management. Th

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Wang, Yuan Gang, Jing Ling Bao, Hui Zhang, and Ke Hua Zou. "The Research of the Odorous Pollutants Dispersion Forecasting and Simulating Model." Advanced Materials Research 225-226 (April 2011): 251–54. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.251.

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Odorous pollution increasingly becomes a serious environmental and social problem. Due to its complexity and a wide range of sources, affected by meteorological, topography, hypsography etc surroundings, being not easy to master its pollution diffusion regularity, odor has been a major hot and difficult problem in the departments of environmental protection administration. According to its characteristics of various composition, odorous pollutants having short time in diffusion way, the higher instantaneous concentrations, the odorous pollutants forecast models are established on the basis of

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Zernov, M., and S. Kholodniuk. "DESIGN OF A SYSTEM FOR ANALYSIS AND FORECASTING OF AIR QUALITY." POLISH JOURNAL OF SCIENCE, no. 79 (October 17, 2024): 56–60. https://doi.org/10.5281/zenodo.13943396.

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The article addresses the issue of air pollution, which poses serious threats to public health and the environment, according to the World Health Organization. The current state of research and technologies in air quality monitoring and forecasting, as well as existing commercial and scientific projects, are described. It is determined that one of the key tasks is providing accessible information to the public about air pollution levels, enabling timely responses to environmental threats. The article presents the development of an intelligent automated system for monitoring and forecasting air

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Evsuykov, Dmitry, Ksenia Degtyareva, Andrei Boyko, and Anatoliy Kukartsev. "Forecasting the environmental safety of water resources using neural networks." E3S Web of Conferences 583 (2024): 02012. http://dx.doi.org/10.1051/e3sconf/202458302012.

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In this study, a neural network model has been developed to analyze the suitability of water based on its chemical and physical characteristics. The ecological significance of the task is due to the need for effective monitoring of the quality of water resources, which are an essential element of ecosystems and directly affect human health and the environment. The model has demonstrated a high accuracy of 87%, which confirms its effectiveness for automated analysis of water quality. The results of the study indicate the importance of parameters such as pH and concentration of pollutants for de

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Sauts, Arthur Valerevich. "Forecasting the environmental effects of air pollution with landfill gas in residential areas." RUDN Journal of Ecology and Life Safety 26, no. 3 (2018): 354–66. http://dx.doi.org/10.22363/2313-2310-2018-26-3-354-366.

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In the article the prediction of ecological consequences of contamination of components of the landfill gas air to a residential building, located on the territory close to the landfill. A method for calculating the dispersion of landfill gas taking into account the influence of the building, based on the numerical solution of differential equations, heat transfer, Reynolds, exponential distribution, supplemented by corresponding models of turbulence, initial and boundary conditions. The technique allows to take into account the spatial heterogeneity of landfills and rolling pins as the source

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Prokhorov, D. O., R. A. Kovalev, K. A. Golovin, and V. P. Safronov. "FORECASTING OF ENVIRONMENTAL POLLUTION AS A RESULT OF DEFORMATIONS OF TECHNOGENIC MINERAL FORMATIONS." News of the Tula state university. Sciences of Earth 4, no. 1 (2021): 76–85. http://dx.doi.org/10.46689/2218-5194-2021-4-1-76-85.

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The issues of the influence of deformations of technogenic mineral formations on the environment are considered. It has been established that all types of deformations of technogenic mineral formations create additional surfaces for the impact of water and wind erosion, and significantly increases the intensity of environmental pollution. A computational mfor predicting deformations is proposed, based on the equations of the method of initial pa-rameters and the method of finite differences.

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