Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review

Human health effects of air pollution

Kampa, M., & Castanas, E. (2008) · Environmental Pollution · doi:10.1016/j.envpol.2007.06.012 · Cited by 4,496 · see citing works

A hierarchical approach to defining marine heatwaves

Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C. J., Benthuysen, J. A., Burrows, M. T., Donat, M. G., Feng, M., et al. (2016) · Progress in Oceanography · doi:10.1016/j.pocean.2015.12.014 · Cited by 2,377 · see citing works

Climate Change 2014 : Synthesis Report

Pachauri, R. K. (. D. )., Allen, M. R. (. D. )., & Minx, J. C. (. D. ). (2015) · OPUS 4 (Zuse Institute Berlin) · Cited by 2,341 · see citing works

Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis

Wu, X., Nethery, R. C., Sabath, M. B., Braun, D., & Dominici, F. (2020) · Science Advances · doi:10.1126/sciadv.abd4049 · Cited by 1,976 · see citing works

Aerodynamic Analysis of SARS-CoV-2 in Two Wuhan Hospitals

Liu, Y., Ning, Z., Chen, Y., Guo, M., Liu, Y., Gali, N. K., Sun, L., Duan, Y., Cai, J., Westerdahl, D., et al. (2020) · Nature · doi:10.1038/s41586-020-2271-3 · Cited by 1,872 · see citing works

An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot

Wernberg, T., Smale, D. A., Tuya, F., Thomsen, M. S., Langlois, T. J., de Bettignies, T., Bennett, S., & Rousseaux, C. S. (2013) · Nature Climate Change · doi:10.1038/nclimate1627 · Cited by 1,304 · see citing works

Investigation of environmental Kuznets curve for ecological footprint: The role of energy and financial development

Destek, M. A., & Sarkodie, S. A. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.017 · Cited by 1,166 · see citing works

A review and evaluation of intraurban air pollution exposure models

Jerrett, M., Arain, M. A., Kanaroglou, P., Beckerman, B., Potoglou, D., Sahsuvaroglu, T., Morrison, J. B., & Giovis, C. (2004) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/sj.jea.7500388 · Cited by 1,131 · see citing works

Exposure to air pollution and COVID-19 mortality in the United States: A nationwide cross-sectional studyPreprint

Wu, X. J., Nethery, R. C., Sabath, M. B., et al. (2020) · medRxiv · doi:10.1101/2020.04.05.20054502 · Cited by 1,043 · see citing works

The Influence of Climate Change on Global Crop Productivity1

Lobell, D., & Gourdji, S. (2012) · Plant Physiology · doi:10.1104/pp.112.208298 · Cited by 919 · see citing works

Association between ambient temperature and COVID-19 infection in 122 cities from China

Xie, J., & Zhu, Y. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138201 · Cited by 807 · see citing works

Links between air pollution and COVID-19 in England

Travaglio, M., Yu, Y., Popovic, R., Selley, L., Leal, N. S., & Martins, L. M. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.115859 · Cited by 627 · see citing works

Supervised autonomous robotic soft tissue surgery

Shademan, A., Decker, R., Opfermann, J. D., Léonard, S., Krieger, A., & Kim, P. C. W. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad9398 · Cited by 626 · see citing works

A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory

Qi, Y., Li, Q., Karimian, H., & Liu, D. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.01.333 · Cited by 609 · see citing works

Health Effects of Ambient Air Pollution in Developing Countries

Mannucci, P. M., & Franchini, M. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14091048 · Cited by 573 · see citing works

Data-Driven Machine Learning in Environmental Pollution: Gains and Problems

Liu, X., Lü, D., Zhang, A., Liu, Q. S., & Jiang, G. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.1c06157 · Cited by 559 · see citing works

Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation

Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., & Wang, J. (2015) · Atmospheric Environment · doi:10.1016/j.atmosenv.2015.02.030 · Cited by 552 · see citing works

Deep Air Quality Forecasting Using Hybrid Deep Learning Framework

Du, S., Li, T., Yang, Y. Z., & Horng, S. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2954510 · Cited by 494 · see citing works

Climate related sea-level variations over the past two millennia

Kemp, A. C., Horton, B. P., Donnelly, J. P., MANN, M. E., Vermeer, M., & Rahmstorf, S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1015619108 · Cited by 478 · see citing works

A novel spatiotemporal convolutional long short-term neural network for air pollution prediction

Wen, C., Liu, S., Yao, X., Peng, L., Xiang, L., Hu, Y. C., & Chi, T. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.11.086 · Cited by 429 · see citing works

Air Pollution Forecasts: An Overview

Bai, L., Wang, J., Ma, X., & Lu, H. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15040780 · Cited by 414 · see citing works

Tackling environmental challenges in pollution controls using artificial intelligence: A review

Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J., & Wang, J. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134279 · Cited by 375 · see citing works

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

Iqbal, M. J., Javed, Z., Sadia, H., Qureshi, I. A., Irshad, A., Ahmed, R., Malik, K. A., Raza, S., Abbas, A., Pezzani, R., et al. (2021) · Cancer Cell International · doi:10.1186/s12935-021-01981-1 · Cited by 369 · see citing works

Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach

Xiao, C., Chen, N., Hu, C., Wang, K., Gong, J., & Chen, Z. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111358 · Cited by 365 · see citing works

Rainfall variability and the trends of wet and dry periods in Bangladesh

Shahid, S. (2009) · International Journal of Climatology · doi:10.1002/joc.2053 · Cited by 333 · see citing works

Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China

Pak, U., Ma, J., Ryu, U., Ryom, K., Juhyok, U., Pak, K., & Pak, C. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.07.367 · Cited by 332 · see citing works

Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model

Zhang, L., Lin, J., Qiu, R., Hu, X., Zhang, H., Chen, Q., Tan, H., Lin, D., & Wang, J. (2018) · Ecological Indicators · doi:10.1016/j.ecolind.2018.08.032 · Cited by 306 · see citing works

IoT enabled environmental toxicology for air pollution monitoring using AI techniques

Asha, P., Natrayan, L., Geetha, B. T., Beulah, J. R., Sumathy, R., Varalakshmi, G., & Neelakandan, S. (2021) · Environmental Research · doi:10.1016/j.envres.2021.112574 · Cited by 303 · see citing works

Air-pollution prediction in smart city, deep learning approach

Bekkar, A., Hssina, B., Douzi, S., & Douzi, K. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00548-1 · Cited by 300 · see citing works

Severe air pollution links to higher mortality in COVID-19 patients: The “double-hit” hypothesis.

Frontera, A., Cianfanelli, L., Vlachos, K. G., Landoni, G., & Cremona, G. (2020) · Journal of Infection · doi:10.1016/j.jinf.2020.05.031 · Cited by 298 · see citing works

A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance

Masood, A., & Ahmad, K. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.129072 · Cited by 276 · see citing works

Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency

Sun, W., & Huang, C. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.130414 · Cited by 270 · see citing works

Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2.5

Ong, B. T., Sugiura, K., & Zettsu, K. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-1955-3 · Cited by 261 · see citing works

Assessing the relationship between ground levels of ozone (O3) and nitrogen dioxide (NO2) with coronavirus (COVID-19) in Milan, Italy

Zoran, M. A., Savastru, R., Savastru, D. M., & Tautan, M. N. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.140005 · Cited by 246 · see citing works

Daily PM 2.5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm

Sun, W., & Sun, J. (2016) · Journal of Environmental Management · doi:10.1016/j.jenvman.2016.12.011 · Cited by 242 · see citing works

Application of the XGBoost Machine Learning Method in PM2.5 Prediction: A Case Study of Shanghai

Ma, J., Yu, Z., Qu, Y., Xu, J., & Cao, Y. (2019) · Aerosol and Air Quality Research · doi:10.4209/aaqr.2019.08.0408 · Cited by 242 · see citing works

Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition

Ali, M., & Prasad, R. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.01.014 · Cited by 240 · see citing works

Rethinking Indian monsoon rainfall prediction in the context of recent global warming

Wang, B., Xiang, B., Li, J., Webster, P. J., Rajeevan, M. N., Liu, J., & Ha, K. (2015) · Nature Communications · doi:10.1038/ncomms8154 · Cited by 237 · see citing works

A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine

Wang, D., Wei, S., Luo, H., Yue, C., & Grunder, O. (2016) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2016.12.018 · Cited by 236 · see citing works

Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network

Wu, Q., & Lin, H. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101657 · Cited by 227 · see citing works

A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

Zhu, D., Cai, C., Yang, T., & Zhou, X. S. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2010005 · Cited by 217 · see citing works

Spatial estimation of urban air pollution with the use of artificial neural network models

Alimissis, A., Philippopoulos, K., Tzanis, C. G., & Deligiorgi, D. (2018) · Atmospheric Environment · doi:10.1016/j.atmosenv.2018.07.058 · Cited by 216 · see citing works

Lung cancer prediction by Deep Learning to identify benign lung nodules

Heuvelmans, M. A., van Ooijen, P. M. A., Ather, S., Silva, C. F., Han, D., Heußel, C. P., Hickes, W., Kauczor, H., Novotný, P., Peschl, H., et al. (2021) · Lung Cancer · doi:10.1016/j.lungcan.2021.01.027 · Cited by 214 · see citing works

AI Methods in Algorithmic Composition: A Comprehensive Survey

Fernandez, J. D., & Vico, F. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3908 · Cited by 212 · see citing works

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration

Shams, S. R., Jahani, A., Kalantary, S., Moeinaddini, M., & Khorasani, N. (2021) · Urban Climate · doi:10.1016/j.uclim.2021.100837 · Cited by 192 · see citing works

Intelligent modeling strategies for forecasting air quality time series: A review

Liu, H., Yan, G., Duan, Z., & Chen, C. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106957 · Cited by 177 · see citing works

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)

Park, S., Kim, M., Kim, M., Namgung, H.-G., Kim, K., Cho, K. H., & Kwon, S.-B. (2017) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2017.07.050 · Cited by 176 · see citing works

An ensemble long short-term memory neural network for hourly PM2.5 concentration forecasting

Bai, Y., Zeng, B., Li, C., & Zhang, J. (2019) · Chemosphere · doi:10.1016/j.chemosphere.2019.01.121 · Cited by 176 · see citing works

Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens

Chaloulakou, A., Saisana, M., & Spyrellis, N. (2003) · The Science of The Total Environment · doi:10.1016/s0048-9697(03)00335-8 · Cited by 172 · see citing works

A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction

Yang, Z., & Wang, J. (2017) · Environmental Research · doi:10.1016/j.envres.2017.06.002 · Cited by 168 · see citing works

PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors

Zhu, S., Lian, X., Wei, L., Che, J., Shen, X., Yang, L., Qiu, X., Liu, X., Gao, W., Ren, X., et al. (2018) · Atmospheric Environment · doi:10.1016/j.atmosenv.2018.04.004 · Cited by 165 · see citing works

Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED

Goto, T., Camargo, C. A., Faridi, M. K., Yun, B. J., & Hasegawa, K. (2018) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2018.06.062 · Cited by 164 · see citing works

Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting

Zhou, Y., Chang, F., Chang, L., Kao, I.-F., Wang, Y.-S., & Kang, C.-C. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.09.111 · Cited by 160 · see citing works

Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis

Elangasinghe, A., Singhal, N., Dirks, K. N., & Salmond, J. (2014) · Atmospheric Pollution Research · doi:10.5094/apr.2014.079 · Cited by 157 · see citing works

Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence

Mele, M., & Magazzino, C. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-10689-0 · Cited by 157 · see citing works

Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions

Guo, Q., He, Z., Li, S., Li, X., Meng, J., Hou, Z., Liu, J., & Chen, Y. (2020) · Aerosol and Air Quality Research · doi:10.4209/aaqr.2020.03.0097 · Cited by 156 · see citing works

Air quality prediction using CNN+LSTM-based hybrid deep learning architecture

Gılık, A., Öğrencı, A. S., & Özmen, A. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16227-w · Cited by 152 · see citing works

A novel hybrid forecasting model for PM10 and SO2 daily concentrations

Wang, P., Liu, Y., Qin, Z., & Zhang, G. (2014) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2014.10.078 · Cited by 144 · see citing works

Forecasting PM10 in metropolitan areas: Efficacy of neural networks

Fernando, H. J. S., Mammarella, M. C., Grandoni, G., Fedele, P., Marco, R. D., Dimitrova, R. N., & Hyde, P. (2012) · Environmental Pollution · doi:10.1016/j.envpol.2011.12.018 · Cited by 143 · see citing works

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

Polezer, G., Tadano, Y. D. S., Siqueira, H. V., Godoi, A. F. L., Yamamoto, C. I., de André, P. A., Junior, T. M. P., Andrade, M. D. F., Oliveira, A. P., Saldiva, P. H. N., et al. (2018) · Environmental Pollution · doi:10.1016/j.envpol.2017.12.111 · Cited by 141 · see citing works

Response of runoff and soil erosion to erosive rainstorm events and vegetation restoration on abandoned slope farmland in the Loess Plateau region, China

Yue, L., Juying, J., Bingzhe, T., Binting, C., & Li, H. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124694 · Cited by 139 · see citing works

Temporal and regional change of some air pollution parameters in Bursa

Çetin, M., Önaç, A. K., Şevik, H., & Sen, B. (2018) · Air Quality Atmosphere & Health · doi:10.1007/s11869-018-00657-6 · Cited by 136 · see citing works

Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy

Brunelli, U., Piazza, V., Pignato, L., Sorbello, F., & Vitabile, S. (2007) · Atmospheric Environment · doi:10.1016/j.atmosenv.2006.12.013 · Cited by 126 · see citing works

Artificial intelligence and machine learning in respiratory medicine

Mekov, E., Miravitlles, M., & Petkov, R. E. (2020) · Expert Review of Respiratory Medicine · doi:10.1080/17476348.2020.1743181 · Cited by 125 · see citing works

Gaseous Pollutants Formation and Their Harmful Effects on Health and Environment

Najjar, Y. S. H. (2011) · Innovative Energy Policies · doi:10.4303/iep/e101203 · Cited by 124 · see citing works

Temporal evolution characteristics of PM2.5 concentration based on continuous wavelet transform

Chen, X., Yin, L., Fan, Y., Song, L., Ji, T., Liu, Y., Tian, J., & Zheng, W. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134244 · Cited by 122 · see citing works

A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions

Kamińska, J. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.09.196 · Cited by 121 · see citing works

Forecasting carbon emissions due to electricity power generation in Bahrain

Qader, M. R., Khan, S., Kamal, M., Usman, M. Z., & Haseeb, M. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16960-2 · Cited by 116 · see citing works

A model for particulate matter (PM2.5) prediction for Delhi based on machine learning approaches

Masood, A., & Ahmad, K. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.258 · Cited by 112 · see citing works

A new fuzzy time series model based on robust clustering for forecasting of air pollution

Dinçer, N. G., & Akkuş, Ö. (2017) · Ecological Informatics · doi:10.1016/j.ecoinf.2017.12.001 · Cited by 111 · see citing works

Hybrid algorithm for short-term forecasting of PM2.5 in China

Cheng, Y., Zhang, H., Liu, Z., Chen, L., & Wang, P. (2018) · Atmospheric Environment · doi:10.1016/j.atmosenv.2018.12.025 · Cited by 110 · see citing works

Hourly PM2.5 concentration forecast using stacked autoencoder model with emphasis on seasonality

Bai, Y., Li, Y., Zeng, B., Li, C., & Zhang, J. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.03.253 · Cited by 109 · see citing works

Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System

Zeinalnezhad, M., Chofreh, A. G., Goni, F. A., & Klemeš, J. J. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.121218 · Cited by 107 · see citing works

Hybrid interpretable predictive machine learning model for air pollution prediction

Gu, Y., Li, B., & Meng, Q. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.09.051 · Cited by 106 · see citing works

The short-term effects of air pollution on respiratory diseases and lung cancer mortality in Hefei: A time-series analysis

Zhu, F., Ding, R., Lei, R., Cheng, H., Liu, J., Shen, C., Zhang, C., Xu, Y., Xiao, C., Li, X., et al. (2018) · Respiratory Medicine · doi:10.1016/j.rmed.2018.11.019 · Cited by 104 · see citing works

A Change in the Amount of CO2 at the Center of the Examination Halls: Case Study of Turkey

Çetin, M. (2016) · Studies on Ethno-Medicine · doi:10.1080/09735070.2016.11905483 · Cited by 102 · see citing works

Development of artificial intelligence based NO 2 forecasting models at Taj Mahal, Agra

Mishra, D., & Goyal, P. (2014) · Atmospheric Pollution Research · doi:10.5094/apr.2015.012 · Cited by 100 · see citing works

Statistical and machine learning ensemble modelling to forecast sea surface temperature

Wolff, S., O’Donncha, F., & Chen, B. (2020) · Journal of Marine Systems · doi:10.1016/j.jmarsys.2020.103347 · Cited by 100 · see citing works

Forecasting concentrations of air pollutants using support vector regression improved with particle swarm optimization: Case study in Aburrá Valley, Colombia

Murillo-Escobar, J. P., Suescun, J. P. S., Correa, M., & Orrego, D. (2019) · Urban Climate · doi:10.1016/j.uclim.2019.100473 · Cited by 98 · see citing works

Onset and Decline Rates of Marine Heatwaves: Global Trends, Seasonal Forecasts and Marine Management

Spillman, C. M., Smith, G., Hobday, A. J., & Hartog, J. R. (2021) · Frontiers in Climate · doi:10.3389/fclim.2021.801217 · Cited by 96 · see citing works

Air quality forecasting with hybrid LSTM and extended stationary wavelet transform

Zeng, Y., Chen, J., Jin, N., Jin, X., & Du, Y. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.108822 · Cited by 96 · see citing works

Air PM2.5 concentration multi-step forecasting using a new hybrid modeling method: Comparing cases for four cities in China

Liu, H., Jin, K., & Duan, Z. (2019) · Atmospheric Pollution Research · doi:10.1016/j.apr.2019.05.007 · Cited by 95 · see citing works

Coronary Heart Disease Diagnosis Through Self-Organizing Map and Fuzzy Support Vector Machine with Incremental Updates

Nilashi, M., Ahmadi, H., Manaf, A. A., Rashid, T. A., Samad, S., Shahmoradi, L., Aljojo, N., & Akbari, E. (2020) · International Journal of Fuzzy Systems · doi:10.1007/s40815-020-00828-7 · Cited by 93 · see citing works

Forecasting PM2.5 induced male lung cancer morbidity in China using satellite retrieved PM2.5 and spatial analysis

Han, X., Liu, Y., Gao, H., Ma, J., Mao, X., Wang, Y., & Ma, X. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.07.061 · Cited by 91 · see citing works

Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia

Zaman, N. A. F. K., Kanniah, K. D., & Kaskaoutis, D. G. (2017) · Atmospheric Research · doi:10.1016/j.atmosres.2017.04.019 · Cited by 90 · see citing works

Are Peak Summer Sultry Heat Wave Days over the Yangtze–Huaihe River Basin Predictable?

Gao, M., Wang, B., Yang, J., & Dong, W. (2017) · Journal of Climate · doi:10.1175/jcli-d-17-0342.1 · Cited by 88 · see citing works

Air Pollution and Asthma

Pfeffer, P., Mudway, I., & Grigg, J. (2020) · CHEST Journal · doi:10.1016/j.chest.2020.10.053 · Cited by 87 · see citing works

Integrated artificial neural network prediction model of indoor environmental quality in a school building

Cho, J. H., & Moon, J. W. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131083 · Cited by 86 · see citing works

Two-step-hybrid model based on data preprocessing and intelligent optimization algorithms (CS and GWO) for NO2 and SO2 forecasting

Zhu, S., Qiu, X., Yin, Y., Fang, M., Liu, X., Zhao, X., & Shi, Y. (2019) · Atmospheric Pollution Research · doi:10.1016/j.apr.2019.03.004 · Cited by 84 · see citing works

Carbon dioxide emissions prediction of five Middle Eastern countries using artificial neural networks

Ahmadi, M. H., Jashnani, H., Chau, K., Kumar, R., & Rosen, M. A. (2019) · Energy Sources Part A Recovery Utilization and Environmental Effects · doi:10.1080/15567036.2019.1679914 · Cited by 81 · see citing works

A hybrid multi-resolution multi-objective ensemble model and its application for forecasting of daily PM2.5 concentrations

Liu, H., Duan, Z., & Chen, C. (2019) · Information Sciences · doi:10.1016/j.ins.2019.12.054 · Cited by 80 · see citing works

Periodical and regional change of particulate matter and CO2 concentration in Misurata

Elsunousi, A. A. M., Şevik, H., Çetin, M., Özel, H. B., & Özel, H. U. (2021) · Environmental Monitoring and Assessment · doi:10.1007/s10661-021-09478-0 · Cited by 80 · see citing works

A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration

Gan, K., Sun, S., Wang, S., & Wei, Y. (2018) · Atmospheric Pollution Research · doi:10.1016/j.apr.2018.03.008 · Cited by 79 · see citing works

Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China

Xu, Y., Du, P., & Wang, J. (2017) · Environmental Pollution · doi:10.1016/j.envpol.2017.01.043 · Cited by 76 · see citing works

Research and application of a novel hybrid air quality early-warning system: A case study in China

Li, C., & Zhu, Z. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.01.195 · Cited by 74 · see citing works

Prediction of SO2 and PM10 air pollutants using a deep learning-based recurrent neural network: Case of industrial city Sakarya

Kurnaz, G., & Demir, A. S. (2021) · Urban Climate · doi:10.1016/j.uclim.2021.101051 · Cited by 74 · see citing works

Predicting the Evolution of the 2014–2016 California Current System Marine Heatwave From an Ensemble of Coupled Global Climate Forecasts

Jacox, M. G., Tommasi, D., Alexander, M. A., Hervieux, G., & Stock, C. A. A. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00497 · Cited by 68 · see citing works

Machine Learning‐Based CO 2 Prediction for Office Room: A Pilot Study

Kapoor, N. R., Kumar, A., Kumar, A., Kumar, A., Mohammed, M. A., Kumar, K., Kadry, S., & Lim, S. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/9404807 · Cited by 66 · see citing works

Estimation of bus emission models for different fuel types of buses under real conditions

Wang, C., Ye, Z., Yu, Y., & Gong, W. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.05.289 · Cited by 64 · see citing works

Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China

Lin, S., Wei, D., Sun, Y., Chen, K., Yang, L., Liu, B., Huang, Q., Paoliello, M. M. B., Li, H., & Wu, S. (2020) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2020.111035 · Cited by 62 · see citing works

Greenhouse effect, sea level rise, and barrier Islands: Case study of long beach Island, New Jersey

Titus, J. G. (1990) · Coastal Management · doi:10.1080/08920759009362101 · Cited by 61 · see citing works

Automated Interpretation of Pulmonary Function Tests in Adults with Respiratory Complaints

Topalovic, M., Laval, S., Aerts, J., Troosters, T., Decramer, M., Janssens, W., & on behalf of the Belgian Pulmonary Function Study investigators (2017) · Respiration · doi:10.1159/000454956 · Cited by 59 · see citing works

Prediction of heat waves over Pakistan using support vector machine algorithm in the context of climate change

Khan, N., Shahid, S., Ismail, T. B., & Behlil, F. (2021) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01963-1 · Cited by 59 · see citing works

The association between COVID-19 deaths and short-term ambient air pollution/meteorological condition exposure: a retrospective study from Wuhan, China

Jiang, Y., & Xu, J. (2020) · Air Quality Atmosphere & Health · doi:10.1007/s11869-020-00906-7 · Cited by 58 · see citing works

Application of computational intelligence techniques to forecast daily PM 10 exceedances in Brunei Darussalam

Dotse, S.-Q., Petra, M. I., Dagar, L., & Silva, L. C. D. (2017) · Atmospheric Pollution Research · doi:10.1016/j.apr.2017.11.004 · Cited by 55 · see citing works

Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods

EJOHWOMU, O. A., Oshodi, O. S., Oladokun, M. O., Bukoye, O. T., Emekwuru, N. G., Sotunbo, A. S., & Adenuga, O. A. (2022) · Buildings · doi:10.3390/buildings12010046 · Cited by 55 · see citing works

Application of Artificial Intelligence in Modern Healthcare System

Datta, S., Barua, D. R., & Das, J. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.90454 · Cited by 53 · see citing works

Prediction of ozone levels using a Hidden Markov Model (HMM) with Gamma distribution

Zhang, H., Zhang, W., Palazoğlu, A., & Sun, W. H. (2012) · Atmospheric Environment · doi:10.1016/j.atmosenv.2012.08.008 · Cited by 51 · see citing works

Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study

Xiang, Y., Ji, H., Zhou, Y., Li, F., Du, J., Rasmy, L., Wu, S., Zheng, W. J., Xu, H., Zhi, D., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/16981 · Cited by 51 · see citing works

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

Wang, J., Niu, T., & Wang, R. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14030249 · Cited by 50 · see citing works

Air Quality Prediction based on LSTM-Kalman Model

Song, X., Huang, J., & Song, D. (2019) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic.2019.8785751 · Cited by 49 · see citing works

A spatial multi-resolution multi-objective data-driven ensemble model for multi-step air quality index forecasting based on real-time decomposition

Liu, H., & Yang, R. (2021) · Computers in Industry · doi:10.1016/j.compind.2020.103387 · Cited by 47 · see citing works

A Big Data and Artificial Intelligence Framework for Smart and Personalized Air Pollution Monitoring and Health Management in Hong Kong

Li, V. O. K., Lam, J. C. K., Han, Y., & Chow, K. (2021) · Environmental Science & Policy · doi:10.1016/j.envsci.2021.06.011 · Cited by 45 · see citing works

Air Quality Predictive Modeling Based on an Improved Decision Tree in a Weather-Smart Grid

Wang, Y., & Kong, T. (2019) · IEEE Access · doi:10.1109/access.2019.2956599 · Cited by 44 · see citing works

Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants

Althuwaynee, O. F., Balogun, A., & Madhoun, W. A. (2020) · GIScience & Remote Sensing · doi:10.1080/15481603.2020.1712064 · Cited by 44 · see citing works

The gene repertoire of the main cysteine protease of Trypanosoma cruzi, cruzipain, reveals four sub-types with distinct active sites

Santos, V. C., Oliveira, A. E. R., Campos, A. C. B., Reis-Cunha, J. L., Bartholomeu, D. C., Teixeira, S. M. R., Lima, A. P. C. A., & Ferreira, R. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-97490-2 · Cited by 44 · see citing works

Subseasonal prediction of the 2020 Great Barrier Reef and Coral Sea marine heatwave

Benthuysen, J. A., Smith, G., Spillman, C. M., & Steinberg, C. R. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/ac3aa1 · Cited by 44 · see citing works

Using Deep Learning Techniques to Forecast Environmental Consumption Level

Lee, D.-H., Kang, S., & Shin, J. (2017) · Sustainability · doi:10.3390/su9101894 · Cited by 43 · see citing works

A Novel Air Quality Early-Warning System Based on Artificial Intelligence

Mo, X., Zhang, L., Li, H., & Qu, Z. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16193505 · Cited by 42 · see citing works

Air pollution and cardiorespiratory hospitalization, predictive modeling, and analysis using artificial intelligence techniques

Usmani, R. S. A., Pillai, T. R., Hashem, M., Marjani, M., Shaharudin, R., & Latif, M. T. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-14305-7 · Cited by 42 · see citing works

Classification Prediction of PM10 Concentration Using a Tree-Based Machine Learning Approach

Shaziayani, W. N., Ul–Saufie, A. Z., Mutalib, S., Noor, N. M., & Zainordin, N. S. (2022) · Atmosphere · doi:10.3390/atmos13040538 · Cited by 41 · see citing works

Air Quality Forecasting in Madrid Using Long Short-Term Memory Networks

Pardo, E., & Malpica, N. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59773-7_24 · Cited by 39 · see citing works

Adaptive neuro-fuzzy prediction of carbon monoxide emission from a clinical waste incineration plant

Norhayati, I., & Rashid, M. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-2921-z · Cited by 38 · see citing works

Ambient Air Pollution and Daily Hospital Admissions for Respiratory Disease in Children in Guiyang, China

Zhou, H., Wang, T., Zhou, F., Liu, Y., Zhao, W., Wang, X., Chen, H., & Cui, Y. (2019) · Frontiers in Pediatrics · doi:10.3389/fped.2019.00400 · Cited by 36 · see citing works

Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece

Moustris, K. P., Douros, K., Nastos, P. T., Larissi, I. K., Anthracopoulos, M. Β., Paliatsos, A. G., & Priftis, K. Ν. (2011) · International Journal of Environmental Health Research · doi:10.1080/09603123.2011.605876 · Cited by 35 · see citing works

Optimized neural network for daily-scale ozone prediction based on transfer learning

Ma, W., Yuan, Z., Lau, A. K. H., Wang, L., Liao, C., & Zhang, Y. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.154279 · Cited by 35 · see citing works

Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease

Guerra, B., Haile, S. R., Lamprecht, B., Ramírez, A. S., Martínez‐Camblor, P., Kaiser, B., Alfageme, I., Almagro, P., Casanova, C., Esteban, C., et al. (2018) · BMC Medicine · doi:10.1186/s12916-018-1013-y · Cited by 34 · see citing works

Artificial Intelligence and Climate Change

Stein, A. L. (2020) · bepress Legal Repository · Cited by 34 · see citing works

The rise in the atmospheric concentration of carbon dioxide and the effects on human health

Robertson, D. S. (2001) · Medical Hypotheses · doi:10.1054/mehy.2000.1256 · Cited by 33 · see citing works

Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain

González-Pardo, J., Ceballos‐Santos, S., Manzanas, R., Santibáñez, M., & Fernández-Olmo, I. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.153786 · Cited by 33 · see citing works

Granger-Causality-based air quality estimation with spatio-temporal (S-T) heterogeneous big data

Zhu, J. Y., Sun, C., & Li, V. O. K. (2015) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2015.7179453 · Cited by 32 · see citing works

Soft Computing Applications in Air Quality Modeling: Past, Present, and Future

Rahman, M. M., Shafiullah, M., Rahman, S. M., Khondaker, A. N., Amao, A. O., & Zahir, M. H. (2020) · Sustainability · doi:10.3390/su12104045 · Cited by 31 · see citing works

A Novel Method for Regional NO2 Concentration Prediction Using Discrete Wavelet Transform and an LSTM Network

Liu, B., Zhang, L., Wang, Q., & Chen, J. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/6631614 · Cited by 31 · see citing works

Prediction of air pollutants by using an artificial neural network

Sohn, S. H., Oh, S. C., & Yeo, Y. (1999) · Korean Journal of Chemical Engineering · doi:10.1007/bf02707129 · Cited by 29 · see citing works

A Gaussian process mixture model-based hard-cut iterative learning algorithm for air quality prediction

Zhou, Y., Zhao, X., Lin, K.-P., Wang, C.-H., & Li, L. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105789 · Cited by 29 · see citing works

A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China

Du, P., Wang, J., Yang, W., & Niu, T. (2021) · Journal of Forecasting · doi:10.1002/for.2785 · Cited by 27 · see citing works

Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation

Conibear, L., Reddington, C. L. S., Silver, B. J., Chen, Y., Knote, C., Arnold, S. R., & Spracklen, D. V. (2022) · GeoHealth · doi:10.1029/2021gh000570 · Cited by 24 · see citing works

Application of Artificial Intelligence in Curbing Air Pollution: The Case of India

Aayush, K., Vishal, D., Hammad, N., & Manu, K. S. (2020) · Asian Journal of Management · doi:10.5958/2321-5763.2020.00044.x · Cited by 22 · see citing works

Combining machine learning models through multiple data division methods for PM2.5 forecasting in Northern Xinjiang, China

Ren, M., Sun, W., & Chen, S. (2021) · Environmental Monitoring and Assessment · doi:10.1007/s10661-021-09233-5 · Cited by 21 · see citing works

Influence of air pollution on pulmonary function in healthy young men from different regions of Poland.

Lubiński, W., Toczyska, I., Chciałowski, A., & Płusa, T. (2005) · PubMed · Cited by 20 · see citing works

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

Park, Y., Lee, C., & Jung, J. Y. (2022) · Yonsei Medical Journal · doi:10.3349/ymj.2022.63.s1 · Cited by 19 · see citing works

Correlation of air pollution and meteorological data using neural networks

Slini, T., Karatzas, K. D., & MOUSSIOPOULOS, N. (2003) · International Journal of Environment and Pollution · doi:10.1504/ijep.2003.004279 · Cited by 18 · see citing works

Artificial Intelligence Applications in handling the Infectious Diseases

Jain, K. K. (2020) · doi:10.35248/2167-1079.20.10.351 · Cited by 15 · see citing works

Developing a wavelet-AI hybrid model for short- and long-term predictions of the pollutant concentration of particulate matter10

Mirzadeh, S. M., Nejadkoorki, F., Mirhoseini, S. A., & Moosavi, V. (2021) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-020-03123-y · Cited by 14 · see citing works

Fuzzy binary relation based elucidation of air quality over a highly polluted urban region of India

Chattopadhyay, G., Chattopadhyay, S., & Midya, S. K. (2021) · Earth Science Informatics · doi:10.1007/s12145-021-00625-2 · Cited by 14 · see citing works

Spatio-temporal variation and daily prediction of PM2.5 concentration in world-class urban agglomerations of China

Yan, D., Kong, Y., Ye, B., & Xiang, H. (2020) · Environmental Geochemistry and Health · doi:10.1007/s10653-020-00708-x · Cited by 13 · see citing works

Multi-Model Ensemble Forecast Method of PM2.5 Concentration Based on Wavelet Neural Networks

Li, T., Xiang, L., Wang, L., Ren, Y., Zhang, T., & Yu, M. (2018) · International Cognitive Cities Conference (IC3) · doi:10.1109/ic3.2018.00026 · Cited by 10 · see citing works

The Role of Renewable Energy in Mitigating Deforestation and Climate Change in Nigeria

Maiwada, N. A., El-ladan, A. H., & Abdullahi, U. (2014) · Journals & Books Hosting (International Knowledge Sharing Platform) · Cited by 6 · see citing works

Efficient downscaling of satellite oceanographic data with convolutional neural networks

Saxena, N. (2021) · SIGSPATIAL Special · doi:10.1145/3447994.3448011 · Cited by 6 · see citing works

Carbon Dioxide Emission Prediction of Five Middle Eastern Countries by Using Artificial Neural NetworkPreprint

Ahmadi, M. H., Jashnani, H., Salwana, E., et al. (2019) · Preprints.org · doi:10.20944/preprints201905.0346.v1 · Cited by 6 · see citing works

Study on Gray Numerical Model of Air Pollution in Wuan City

Zhou, J.-H., Zhao, J.-G., & Li, P. (2010) · International Conference on Challenges in Environmental Science and Computer Engineering · doi:10.1109/cesce.2010.30 · Cited by 5 · see citing works

Machine Learning-Based Air Pollution Prediction

Shivakumar, S., Shastry, K. A., Singh, S., Pasha, S., Vinay, B. C., & Sushma, V. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-3342-3_2 · Cited by 2 · see citing works

Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions

Olawade, D. B., Wada, O. Z., Ige, A. O., Egbewole, B. I., Olojo, A. S., & Oladapo, B. I. (2024) · Hygiene and Environmental Health Advances · doi:10.1016/j.heha.2024.100114 · Cited by 387 · see citing works

Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts

Chen, F., Zhang, W., Mfarrej, M. F. B., Saleem, M. H., Khan, K. A., Ma, J., Raposo, A., & Han, H. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.116532 · Cited by 173 · see citing works

AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change

Jain, H., Dhupper, R., Shrivastava, A., Kumar, D., & Kumari, M. (2023) · Computational Urban Science · doi:10.1007/s43762-023-00100-2 · Cited by 136 · see citing works

Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review

Chadalavada, S., Faust, O., Salvi, M., Seoni, S., Raj, N., Raghavendra, U., Gudigar, A., Barua, P. D., Molinari, F., & Acharya, U. R. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.106312 · Cited by 78 · see citing works

AI-driven participatory environmental management: Innovations, applications, and future prospects

Santos, M. R. C., & Carvalho, L. C. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.123864 · Cited by 77 · see citing works

Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions

Chen, S., Yu, J., Chamouni, S., Wang, Y., & Li, Y. (2024) · BMC Medicine · doi:10.1186/s12916-024-03566-x · Cited by 73 · see citing works

Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review

Yadav, V., Yadav, A. K., Singh, V., & Singh, T. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102305 · Cited by 62 · see citing works

Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features

Ahmed, I. A., Senan, E. M., Shatnawi, H. S. A., Alkhraisha, Z. M., & Al-Azzam, M. M. A. (2023) · Diagnostics · doi:10.3390/diagnostics13040814 · Cited by 47 · see citing works

Nanoplasmonic biosensors for environmental sustainability and human health

Liu, W., Chung, K., Yu, S., & Lee, L. P. (2024) · Chemical Society Reviews · doi:10.1039/d3cs00941f · Cited by 42 · see citing works

Artificial intelligence: A key fulcrum for addressing complex environmental health issues

Huang, L., Duan, Q., Liu, Y., Wu, Y., Li, Z., Guo, Z., Liu, M., Lu, X., Wang, P., Liu, F., et al. (2025) · Environment International · doi:10.1016/j.envint.2025.109389 · Cited by 42 · see citing works

An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan

Wong, P.-Y., Su, H.-J. J., Lung, S. C., & Wu, C. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.161336 · Cited by 41 · see citing works

Machine Learning-Based Assessment of the Influence of Nanoparticles on Biodiesel Engine Performance and Emissions: A critical review

Pawar, C., Shreeprakash, B., Mokshanatha, B., Nikam, K. C., Motgi, N., Jathar, L. D., Shelare, S. D., Sharma, S. K., Dwivedi, S. P., Bains, P. S., et al. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10144-0 · Cited by 39 · see citing works

Is There a Conflict between Automation and Environment? Implications of Artificial Intelligence for Carbon Emissions in China

Xu, X., & Song, Y.-C. (2023) · Sustainability · doi:10.3390/su151612437 · Cited by 39 · see citing works

AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems

Felici-Castell, S., Segura-García, J., Pérez-Solano, J. J., Fayos-Jordán, R., Soriano, A., & Calero, J. M. A. (2023) · Sensors · doi:10.3390/s23239585 · Cited by 38 · see citing works

Artificial Intelligence in Ecology: A Commentary on a Chatbot's Perspective

Haghighi, S. R., Saqalaksari, M. P., & Johnson, S. N. (2023) · Bulletin of the Ecological Society of America · doi:10.1002/bes2.2097 · Cited by 35 · see citing works

Next-Generation Air Quality Management: Unveiling Advanced Techniques for Monitoring and Controlling Pollution

Kumari, S., Choudhury, A., Karki, P., Simon, M., Chowdhry, J., Nandra, A., Sharma, P., Sengupta, A., Yadav, A., Raju, M. P., et al. (2025) · Aerosol Science and Engineering · doi:10.1007/s41810-024-00281-1 · Cited by 27 · see citing works

Air pollution and cardiovascular diseases: mechanisms, evidence, and mitigation strategies

Krismanuel, H. (2025) · Journal of Medicine and Life · doi:10.25122/jml-2025-0018 · Cited by 26 · see citing works

Evaluating air quality and criteria pollutants prediction disparities by data mining along a stretch of urban-rural agglomeration includes coal-mine belts and thermal power plants

Choudhary, A., Kumar, P., Pradhan, C., Sahu, S. K., Chaudhary, S. K., Joshi, P. K., Pandey, D. N., Prakash, D., & Mohanty, A. (2023) · Frontiers in Environmental Science · doi:10.3389/fenvs.2023.1132159 · Cited by 25 · see citing works

Evaluating the Impact of AI-Based Sustainability Measures in Industry 5.0: A Longitudinal Study

Викторовна, Г. В., Kirola, M., Gupta, M., Bharathi, P., & Acharya, P. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/20248601058 · Cited by 23 · see citing works

A Robust Tuberculosis Diagnosis Using Chest X-Rays Based on a Hybrid Vision Transformer and Principal Component Analysis

El-Ghany, S. A., Elmogy, M. M., Mahmood, M. A., & El-Aziz, A. A. A. (2024) · Diagnostics · doi:10.3390/diagnostics14232736 · Cited by 21 · see citing works

Integrating data-driven and physics-based approaches for robust wind power prediction: A comprehensive ML-PINN-Simulink framework

Rajaperumal, T. A., & Christopher Columbus, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-13306-7 · Cited by 21 · see citing works

Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models

Zaman, N. A. F. K., Kanniah, K. D., Kaskaoutis, D. G., & Latif, M. T. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.141559 · Cited by 19 · see citing works

Exploring the relationship between air quality index and lung cancer mortality in India: predictive modeling and impact assessment

Singh, T., Kaur, A., Katyal, S. K., Walia, S. K., Dhand, G., Sheoran, K., Mohanty, S. N., Khan, M. I., Awwad, F. A. A., & Ismail, E. A. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-47705-5 · Cited by 18 · see citing works

A Comprehensive Review of Data-Driven Techniques for Air Pollution Concentration Forecasting

Bernacki, J., & Scherer, R. (2025) · Sensors · doi:10.3390/s25196044 · Cited by 17 · see citing works

Exploring the Detailed Spatiotemporal Characteristics of Pm2.5: Generating a Full-Coverage and Hourly Pm2.5 Dataset in the Sichuan Basin, China

Zhai, S., Zhang, Y., Huang, J., Li, X., Wang, W., Zhang, T., Yin, F., & Ma, Y. (2022) · Chemosphere · doi:10.1016/j.chemosphere.2022.136786 · Cited by 16 · see citing works

The role of artificial intelligence in environmental sustainability

Patoucha, A., & Γαρείου, Ζ. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202458511011 · Cited by 20 · see citing works

Space-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks

Peralta, B., Sepúlveda, T., Nicolis, O., & Caro, L. (2022) · Applied Sciences · doi:10.3390/app122211317 · Cited by 16 · see citing works

Adopting GPU computing to support DL-based Earth science applications

Wang, Z., Li, Y., Wang, K., Cain, J., Salami, M., Duffy, D. Q., Little, M. M., & Yang, C. P. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2023.2233488 · Cited by 14 · see citing works

Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air

Yang, J., Ke, H., Gong, S., Wang, Y., Zhang, L., Zhou, C., Mo, J., & You, Y. (2025) · Earth and Space Science · doi:10.1029/2024ea003942 · Cited by 17 · see citing works

Harnessing artificial intelligence for sustainable environmental remediation a review

Balakumar, S., Narayanan, M., Kamaraj, M., & Aravind, J. (2025) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-025-06528-9 · Cited by 17 · see citing works

Evaluation of different machine learning approaches for predicting high concentration episodes of ground-level ozone: A case study in Catalonia, Spain

Vicente, D. J., Salazar, F., López-Chacón, S. R., Soriano, C., & Vide, J. M. (2023) · Atmospheric Pollution Research · doi:10.1016/j.apr.2023.101999 · Cited by 13 · see citing works

Air Pollution Forecasting Using Genetic Algorithm and LSTM Deep Learning Technique

Rastogi, U., Kumar, S., Maurya, S. P., & Yadav, A. K. (2025) · Aerosol Science and Engineering · doi:10.1007/s41810-025-00343-y · Cited by 12 · see citing works

International and Constitutional Efforts to Protect the Environment Through the Use of Artificial Intelligence Techniques

Hammouri, A. A., Al-Billeh, T. N., Al-Billeh, H., Alhammouri, N., & Belghit, R. (2024) · Pakistan Journal of Criminology · doi:10.62271/pjc.16.1.387.398 · Cited by 12 · see citing works

Sustainable Application of Artificial Intelligence in Biomonitoring for Environmental Sustainability: Challenges and Prospects

Hait, M., Sahu, P., Biswas, S., & Izah, S. C. (2024) · Springer eBooks · doi:10.1007/978-981-97-1658-6_22 · Cited by 12 · see citing works

Enhancing water and air pollution monitoring and control through ChatGPT and similar generative artificial intelligence implementationPreprint

Rane, N., Choudhary, S. P., & Rane, J. (2024) · Social Science Research Network · doi:10.2139/ssrn.4681733 · Cited by 9 · see citing works

Explainable Artificial Intelligence and Machine Learning for Air Pollution Risk Assessment and Respiratory Health Outcomes: A Systematic Review

Agbehadji, I. E., & Obagbuwa, I. C. (2025) · Atmosphere · doi:10.3390/atmos16101154 · Cited by 11 · see citing works

Planning for cooler cities: A multimodal AI framework for hyperlocal spatio-temporal urban heat stress prediction and mitigation

Yi, S., Li, X., Tu, W., & Zhao, T. (2025) · Urban forestry & urban greening · doi:10.1016/j.ufug.2025.129101 · Cited by 14 · see citing works

Artificial intelligence-driven solutions for mitigating human–wildlife conflict in biodiversity hotspots

Ojija, F. S., Ogwu, M. C., Ally, J. S., John, J. P., Lameck, A. S., Felix, N., & Tekka, R. S. (2025) · Science Progress · doi:10.1177/00368504251394584 · Cited by 11 · see citing works

Human-Centric AI Adoption and Its Influence on Worker Productivity: An Empirical Investigation

Shchepkina, N. N., Ramnarayan, Dhaliwal, N. K., Ravikiran, K., & Nangia, R. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/20248601060 · Cited by 18 · see citing works

Impact of cotton dust, endotoxin exposure, and other occupational health risk due to indoor pollutants on textile industry workers in low and middle- income countries

Shankar, S., Raju, N., Ganesan, A., Nithyaprakash, R., Chenniappan, M., Stonier, A. A., Prakash, C., Pramanik, A., & Basak, A. K. (2024) · Journal of Air Pollution and Health · doi:10.18502/japh.v9i1.15080 · Cited by 11 · see citing works

Forecasting air pollutants using classification models: a case study in the Bay of Algeciras (Spain)

Rodríguez-García, M. I., Rodrigues, M. C. R., González-Enrique, J., Ruíz-Aguilar, J. J., & Turias, I. J. (2023) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-023-02512-2 · Cited by 11 · see citing works

Machine learning for modelling the health impacts of extreme heat: A comprehensive literature review.

Boudreault, J., Lamothe, F., Campagna, C., & Chebana, F. (2025) · Environment International · doi:10.1016/j.envint.2025.109965 · Cited by 10 · see citing works

A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention

Liu, D., Tang, Z., & Cai, Y. (2022) · Sustainability · doi:10.3390/su142315522 · Cited by 10 · see citing works

Forecasting Indoor Air Quality Using Machine Learning Models

Singh, A., Islam, M., & Dinh, N. T. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444387 · Cited by 10 · see citing works

Spatio-Temporal Distribution of PM2.5 and PM10 Concentrations and Assessment of Public Health Risk in the Three Most Polluted Provinces of Iran

Abadi, A. R. S., Hamzeh‎, N. H., Kaskaoutis, D. G., Vuillaume, J.-F., Shukurov, K. A., & Gharibzadeh, M. (2024) · Sustainability · doi:10.3390/su17010044 · Cited by 10 · see citing works

Electrocatalysts for enhanced sensing applications: a review of materials, performance metrics, and future prospects

Chettiannan, B., Arumugam, G., Selvaraj, M., Assiri, M. A., Mathan, S., Murugesan, V., & Rajendran, R. (2025) · Journal of Materials Science Materials in Electronics · doi:10.1007/s10854-025-15521-4 · Cited by 8 · see citing works

Biocompatible Nanomaterials for Sustainable Biomedical Applications

Dutt, A., Saini, N., Kalia, A., Madan, P., Srikanth, T., & Talukdar, S. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202454703020 · Cited by 8 · see citing works

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

Flayyih, H. Q., Waleed, J., & Ibrahim, A. M. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/160159 · Cited by 7 · see citing works

Seeding the future: a review of artificial intelligence’s pivotal role in seed technology

Mahant, V., & Pal, P. (2025) · Journal of Crop Science and Biotechnology · doi:10.1007/s12892-025-00284-5 · Cited by 6 · see citing works

Comparative Review of ICAO and EUROCONTROL Flight Carbon Emission Approximators

Rezo, Z., Steiner, S., & Babić, R. Š. (2025) · Sustainability · doi:10.3390/su17146329 · Cited by 6 · see citing works

Harnessing artificial intelligence for food security in a changing climate

Bhairo‐Beekhoo, P., & Maharaj, R. (2025) · Discover Food · doi:10.1007/s44187-025-00609-x · Cited by 9 · see citing works

AI-Powered Smart Cities: Data-Driven Approaches for Urban Innovation and Sustainability

Parlapalli, V., Ganeeb, K. K., Bidkar, D. M., Veerapaneni, P. K., Sankiti, S. R., & Harve, B. M. (2025) · International Conference on Communication, Computing, Networking, and Control in Cyber-Physical Systems (CCNCPS) · doi:10.1109/ccncps66785.2025.11135701 · Cited by 5 · see citing works

Evaluating Machine Learning Models for Particulate Matter Prediction Under Climate Change Scenarios in Brazilian Capitals

Bonifácio, A. D. S., Tavella, R. A., Brum, R. D. L., Silveira, G. D. O., Fernandes, R. C., Scursone, G. F., Machado, R. A., Adamatti, D. F., & Júnior, F. M. R. D. S. (2025) · Atmosphere · doi:10.3390/atmos16091052 · Cited by 5 · see citing works

Microplastics in Antarctic air: revealing current findings

Deylami, S., Cárdenas-Escudero, J., Ochoa, M., Ayuso-Haro, J., Galán‐Madruga, D., Ruiz, J. L. U., & Cáceres, J. O. (2025) · Antarctic Science · doi:10.1017/s0954102024000555 · Cited by 5 · see citing works

A Comprehensive Analysis of Air Pollution in Dhaka City, Bangladesh, and the Application of Artificial Intelligence and Machine Learning for Enhanced Management and Forecasting

Islam, F. A. S. (2025) · International Journal of Applied and Natural Sciences · doi:10.61424/ijans.v3i1.303 · Cited by 5 · see citing works

Artificial Intelligence Application in Nonpoint Source Pollution Management: A Status Update

Morain, A., Nedd, R., Poole, K., Hawkins, L., Jones, M., Washington, B., & Anandhi, A. (2025) · Sustainability · doi:10.3390/su17135810 · Cited by 5 · see citing works

Air Pollution Data and Forecasting Data Monitored through Google Cloud Services by using Artificial Intelligence and Machine Learning

Srivastava, A., Ahmad, A., Kumar, S., & Ahmad, M. A. (2022) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca55336.2022.10009293 · Cited by 5 · see citing works

Mitigating Climate Change

Sahu, S., Mallick, N., & Patnaik, S. (2024) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3693-6336-2.ch007 · Cited by 5 · see citing works

Artificial intelligence and carbon neutrality: A dual role in emissions forecasting, energy consumption, and sustainable strategies

Qiao, Y., Min, X., Ge, S., Zhu, X., & Yang, N. (2026) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2026.122522 · Cited by 7 · see citing works

Modeling and Optimization of NO2 Stations in the Smart City of Barcelona

Soriano-Gonzalez, R., Martin, X. A., Bernabeu, E. P., & Carracedo, P. (2024) · Applied Sciences · doi:10.3390/app142210355 · Cited by 4 · see citing works

Forecasting the air pollution concentration with neural networks

Bernacki, J. (2024) · Urban Climate · doi:10.1016/j.uclim.2024.102262 · Cited by 4 · see citing works

Optimizing Air Pollution Forecasting Models Through Knowledge Distillation: A Novel GCN and TRANS_GRU Methodology for Indian Cities

Kumar, S., Kour, V., Raj, A., Tapung, T., Mishra, S., Misra, R., & Singh, T. N. (2025) · IEEE Access · doi:10.1109/access.2025.3546504 · Cited by 4 · see citing works

Data-Driven Decision Making: Real-world Effectiveness in Industry 5.0 – An Experimental Approach

Rinat, K., Koli, S., Sobti, R., Ledalla, S., & Arora, R. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/20248601061 · Cited by 4 · see citing works

A Study on Different Hybrid Deep Learning Approaches to Forecast air Pollution Concentration of Particulate Matter

Priscila, S. S., & Jayanthiladevi, A. (2023) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs57279.2023.10113037 · Cited by 4 · see citing works

Smart indoor air quality monitoring for enhanced living environments and ambient assisted living

Saini, J., Dutta, M., & Marques, G. (2023) · Advances in computers · doi:10.1016/bs.adcom.2023.10.008 · Cited by 4 · see citing works

Experimental investigation on natural fiber material for pesticide spraying mobile robot structure

Meshram, A., Vanalkar, A. V., Kalambe, K., Badar, A., Kaushik, V., & Mehta, G. D. (2024) · Materials Today Proceedings · doi:10.1016/j.matpr.2024.04.064 · Cited by 4 · see citing works

Air Pollution Prediction Using Long Short-Term Memory Variants

Rahmadeyan, A., Mustakim, M., Erkamim, M., Ahmad, I., Sepriano, S., & Aziz, S. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-59707-7_11 · Cited by 4 · see citing works

Particulate matter pollution prone-areas mapping using an enhanced explainable ensemble meta-algorithm

Razavi-Termeh, S. V., Sadeghi‐Niaraki, A., Ali, F., Pirasteh, S., & Choi, S.-M. (2025) · International Journal of Digital Earth · doi:10.1080/17538947.2025.2557511 · Cited by 3 · see citing works

A Hybrid HYSPLIT-LSTM approach for predicting radioactive material dispersion from the Zaporizhzhia nuclear power plant

Hassanpour, M., Rezaie, M. R., Tayebi, J., Hassanpour, M., Faruque, M. R. I., Khandaker, M. U., & Bradley, D. A. (2025) · Radiation Physics and Chemistry · doi:10.1016/j.radphyschem.2025.113424 · Cited by 3 · see citing works

Advancements in Biochar Functionalization for Sustainable Adsorption of Sulfur‐Containing Gaseous Pollutants

Awogbemi, O., & Desai, D. A. (2025) · Engineering Reports · doi:10.1002/eng2.70440 · Cited by 3 · see citing works

Daily PM2.5 concentration forecasting in Mashhad metropolis using long short-term memory (LSTM) neural networks and feature importance analysis

Zoqi, M. J., Aliaqa, A. D., Rasouli, S. J., & Daneshvar, M. R. M. (2026) · Air Quality Atmosphere & Health · doi:10.1007/s11869-026-01892-y · Cited by 3 · see citing works

Advances in air quality modeling through artificial intelligence, machine learning, and deep learning: A comprehensive review

Nelson, D. L., Choi, Y., Payami, M., Singh, D., Dimri, R., Kayastha, S. G., Shahriar, S. A., Islam, R., Salman, A. K., & Park, J. (2026) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2026.181593 · Cited by 3 · see citing works

Combining AI with urban resilience planning: A bibliometric analysis of literature

Praharaj, S. (2026) · City and Environment Interactions · doi:10.1016/j.cacint.2026.100342 · Cited by 3 · see citing works

Polymer Matrix Nanocomposites for Lightweight Sustainable Automotive Parts

Sehgal, A., Sharma, D., Kataria, A., Verma, R., Kumar, C. V., & Mongal, B. N. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202454703013 · Cited by 3 · see citing works

Comprehensive Physical Characterization of Silver Nanoparticles: Multimodal Evaluation of Material Properties

Suri, S., Ghosal, S., Upadhyay, V. V., Kansal, L., Dhyani, M., Lakshmi, A. A., Kumar, R., & Fallah, M. H. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202458801009 · Cited by 3 · see citing works

Integrating Smart Traffic Systems with Real-Time Air Quality Monitoring to Minimize Emissions and Improve Urban Health

Naizabayeva, L., Zaitov, D., & Seilova, N. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.11.156 · Cited by 3 · see citing works

TabulaTime: Novel multimodal deep learning for Acute Coronary Syndrome prediction through environmental and clinical data integration

Zhang, X., Han, P. L., Hassan, S. S. M., Kalra, P. A., Ritchie, J. P., Diver, C., Shorley, J., & White, S. J. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103395 · Cited by 3 · see citing works

Hybrid unorganized machines to estimate the number of hospital admissions caused by PM$$_{10}$$ concentration

Siqueira, H. V., Bacalhau, E. T., Casacio, L., Puchta, E., Alves, T. A., & Tadano, Y. D. S. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-30180-w · Cited by 3 · see citing works

A Combined Approach for Predicting the Distribution of Harmful Substances in the Atmosphere Based on Parameter Estimation and Machine Learning Algorithms

Набенович, М. М., Temirbekov, N., Alimbekova, N., Malgazhdarov, Y., & Yergaliyev, Y. (2023) · Computation · doi:10.3390/computation11120249 · Cited by 3 · see citing works

Deep Learning Algorithms in Industry 5.0: A Comprehensive Experimental Study

Shchepkina, N. N., Chandramauli, A., Ahuja, S., Swaraj, P. P., & Ranjan, R. K. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/20248601067 · Cited by 3 · see citing works

Biodegradable Nanomaterials for Sustainable Food Packaging Applications

Mishra, M., Mittal, A., Negi, G. S., Singh, P., Kaur, S., Prasanna, J. L., & Sharma, P. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202454701014 · Cited by 2 · see citing works

Air pollution forecasting trends and regional patterns in the Asia Pacific through bibliometric analysis

Rana, R., & Kumar, N. (2025) · Discover Environment · doi:10.1007/s44274-025-00392-7 · Cited by 2 · see citing works

Emerging Integration of Artificial Intelligence of Things (AIoT) in Air Pollution Monitoring and Management

Saha, P., Pandit, B., Rai, U., Kalita, R., Kattel, A., & Biswas, P. (2025) · Environmental Quality Management · doi:10.1002/tqem.70273 · Cited by 2 · see citing works

A Deep Ensemble Learning Framework with Envelope-Based Feature Decomposition for $$\text {PM}_{2.5}$$ Prediction in Indian Metropolitan Cities

Kumar, A., Patel, R., Yadav, J., & Singh, M. (2026) · Water Air & Soil Pollution · doi:10.1007/s11270-026-09324-4 · Cited by 2 · see citing works

Real-Time Information Access in Urban Environments: A User Interaction Study Using the Real-Time Information Test

Orlov, A. K., Sehgal, S. S., Bhardwaj, N., Kumari, N., & Bharadwaj, D. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/20248601106 · Cited by 2 · see citing works

Enhancing electric vehicle efficiency through model predictive control of power electronics

Vatin, N. I., & Madhavi, A. (2024) · MATEC Web of Conferences · doi:10.1051/matecconf/202439201168 · Cited by 2 · see citing works

Fabrication of Biodegradable Polymer Nanocomposites for Sustainable Agriculture

Sobti, R., Singh, M. P., Correa, F. S., Brar, M., & Kumar, D. K. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202451101009 · Cited by 2 · see citing works

Nanomaterials for Healthcare Applications: A Sustainable Approach

Singh, H., Dhyani, M., Thakur, R., Rahul, Nagaraju, K. C., & Sonia, S. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202453709008 · Cited by 2 · see citing works

AIR QUALITY MONITORING IN THE WEST KAZAKHSTAN REGION: PRINCIPLES, METHODS, APPROACHES

Salnikov, V., Polyakova, S., Ullman, А., Kauazov, A., Tursumbayeva, M., Kissebayev, D., "Kazhydromet", R., Miskiv, D., Beldeubayev, E., Musralinova, G., et al. (2024) · Hydrometeorology and ecology · doi:10.54668/2789-6323-2024-113-2-128-149 · Cited by 2 · see citing works

Climate Change and Public Health Dynamics: A Bibliometric Study with Comprehensive Review

Raval, S. D., & Muralidharan, K. (2024) · Current World Environment · doi:10.12944/cwe.19.2.6 · Cited by 2 · see citing works

Advancements in Air Pollution Prediction and Classification Models: Exploring Emerging Frontiers

Mittal, A., Arora, S., Sharma, S., & Garg, A. (2024) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs60874.2024.10716970 · Cited by 2 · see citing works

Enhancing Environmental Policy Decisions in Korea and Japan Through AI-Driven Air Pollution Forecast

Kim, Y., Kim, J., Cho, S., Sim, H., & Kim, J. Y. (2024) · Sustainability · doi:10.3390/su162310436 · Cited by 2 · see citing works

A Bibliometric Lens on the Future: How AI continues to transform education institutions

Easouh, F. Y. J., Oweis, T. I., & Makahleh, H. A. (2024) · Human Systems Management · doi:10.1177/01672533241305172 · Cited by 5 · see citing works

Air Quality Management in Smart Cities by leveraging Machine Learning Techniques

Shree, A. N. R., Shankaramma, & Reddy, C. (2024) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs62842.2024.10841590 · Cited by 2 · see citing works

The Synergy of Emergency Alerts and social media: An Evaluation with the Emergency Alert and Social Media Engagement Test

Vafaeva, K. M., Singh, D., Banoth, R., Arora, R., & Sharma, S. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/20248601074 · Cited by 1 · see citing works

Integrating Policy, Governance, and Law in Regulating AI for Sustainable Environmental Protection

Sahu, S., Mallick, N., & Patnaik, S. (2025) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3693-8684-2.ch007 · Cited by 1 · see citing works

Assessing the Impact of Aviation Emissions on Air Quality at a Regional Greek Airport Using Machine Learning

Stefanis, C., Manisalidis, I., Stavropoulou, E., Stavropoulos, A., Tsigalou, C., Voidarou, C. C., Constantinidis, T. C., & Bezirtzoglou, E. (2025) · Toxics · doi:10.3390/toxics13030217 · Cited by 1 · see citing works

Challenges of solar PV and BIPV systems in India: A comprehensive review with challenges analysis

Kumar, M., Prakash, S., Arora, R., & Bobba, P. B. (2025) · AIP conference proceedings · doi:10.1063/5.0271514 · Cited by 1 · see citing works

Artificial neural network modeling for predicting PM10, PM2.5, NOX, and SO2 in coal mining areas

Mishra, A., Prasad, N., Bhattacharya, T., & Lal, B. (2025) · Earth Science Informatics · doi:10.1007/s12145-025-01922-w · Cited by 1 · see citing works

Forecasting Air Pollution with Artificial Intelligence

Rajak, P., Adhikary, S., Bhattacharya, S., & Ganguly, A. (2024) · CRC Press eBooks · doi:10.1201/9781032683805-5 · Cited by 1 · see citing works

Artificial Intelligence-Driven Models for Air Pollution and Related Health Outcomes

Nyayapathi, P. P., Srinivas, N., & Kolli, S. K. (2026) · Springer eBooks · doi:10.1007/978-3-032-13953-5_7

Forecasting Air Pollution for Environment and Good Health Using Artificial Intelligence

Yadav, N., Singh, S., & Sharma, D. (2023) · International Conference on Innovative Sustainable Computational Technologies (CISCT) · doi:10.1109/cisct57197.2023.10351334 · Cited by 6 · see citing works

An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique

Almalawi, A., Alsolami, F. J., Khan, A. I., Alkhathlan, A., Fahad, A., Irshad, K., Qaiyum, S., & Alfakeeh, A. S. (2021) · Environmental Research · doi:10.1016/j.envres.2021.112576 · Cited by 81 · see citing works

Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review

Chadalavada, S., Faust, O., Salvi, M., Seoni, S., Raj, N., Raghavendra, U., Gudigar, A., Barua, P. D., Molinari, F., & Acharya, U. R. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.106312 · Cited by 78 · see citing works