In a local context, sustainable development entails utilizing the current resources—material and immaterial, measurable and immeasurable, popular and unpopular—of the community in a manner that avoids overexploitati...In a local context, sustainable development entails utilizing the current resources—material and immaterial, measurable and immeasurable, popular and unpopular—of the community in a manner that avoids overexploitation and ensures intergenerational equity. This approach prioritizes the safety and health of local citizens, placing communal productivity above corporate profitability. This research aims to assess air quality surrounding 28 chemical industry sites in Baton Rouge, Louisiana, to understand the environmental and health impacts of industrial pollutants, with a focus on environmental justice. Air quality pollutants, including PM2.5, PM10, O3, NO2, CO, and SO2, were monitored for 75 days during the Summer, using the BreezoMeter app. Python, Mapize, and QGIS software technologies were utilized for data analysis and visualization. Findings indicate a reduction in NO2 and CO levels, compared to existing literature. However, the persistent challenge of particulate matter suggests areas for further environmental management efforts. Additionally, the research suggests a significant disparity in air pollution exposure, probably affecting marginalized communities. Although the nature of the study might not fully capture annual pollution trends, the findings highlight the urgent need for the chemical industry to adopt efficient production methods and for policymakers to enhance air quality standards and enforcement, particularly in pollution-sensitive areas. The disproportionate impact of air pollution on vulnerable communities calls for a more inclusive approach to environmental justice, ensuring equitable distribution of clean air benefits and community involvement in pollution management decisions.展开更多
In the state of Sao Paulo, Brazil, public policies regarding the air quality aimed at the welfare of the population are strongly dependent on monitoring conducted by the Sao Paulo State Environmental Company (CETESB),...In the state of Sao Paulo, Brazil, public policies regarding the air quality aimed at the welfare of the population are strongly dependent on monitoring conducted by the Sao Paulo State Environmental Company (CETESB), which can be influenced by faulty monitors and equipment support and cuts in power supply, among others. A research conducted from 1998 to 2008 indicated that a significant portion of the air quality automatic stations in the state of Sao Paulo did not meet the criterion of representativeness of measurements of PM10, NO2, O3, CO and SO2 concentrations which resulted in the classification of some municipalities as the nonattainment area, a situation evidenced for PM10 and O3 parameters. The network unavailability for each parameter was estimated and compared with the monitoring networks operated in Canada and the UK. This paper discusses the implications of the lack of representativeness of measurements in the environmental licensing process of pollution sources from 2008, when by the effect of state law, municipalities have been qualified according to their air quality nonattainment level.展开更多
A monitoring campaign of BTEX (benzene, toluene, ethylbenzene, o- m- and p-xylene) was carried out nearby two tunnel portals in the urban area of Naples with the aim to verify air quality in this kind of urban sites...A monitoring campaign of BTEX (benzene, toluene, ethylbenzene, o- m- and p-xylene) was carried out nearby two tunnel portals in the urban area of Naples with the aim to verify air quality in this kind of urban sites. Sampling was carried out using the active adsorption technique. Sampling time was 1 h. Ambient temperature and traffic flow measurements were carried out during each sampling operation. The results indicate that average benzene concentrations at both sites exceed the limit value of 10 μg/Nm^3 established by the European Community (EC) (Dir. 2000/69). Concentration levels of other BTEX are relatively high as well. A correlation between BTEX concentration and two wheeler vehicle flow was observed.展开更多
This paper proposes a simple method of optimizing Air Quality Monitoring Network (AQMN) using Geographical Information System (GIS), interpolation techniques and historical data. Existing air quality stations are syst...This paper proposes a simple method of optimizing Air Quality Monitoring Network (AQMN) using Geographical Information System (GIS), interpolation techniques and historical data. Existing air quality stations are systematically eliminated and the missing data are filled in using the most appropriate interpolation technique. The interpolated data are then compared with the observed data. Pre-defined performance measures root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r) were used to check the accuracy of the interpolated data. An algorithm was developed in GIS environment and the process was simulated for several sets of measurements conducted in different locations in Riyadh, Saudi Arabia. This methodology proves to be useful to the decision makers to find optimal numbers of stations that are needed without compromising the coverage of the concentrations across the study area.展开更多
Air pollution is a major global issue with widely known harmful effects on human health and the environment. This pollution is a very complex phenomenon given the diversity of pollutants that may be present in the atm...Air pollution is a major global issue with widely known harmful effects on human health and the environment. This pollution is a very complex phenomenon given the diversity of pollutants that may be present in the atmosphere. The air quality in urban areas is of a great concern for residents living in cities and represents a current issue that requires an adequate management. So that air quality policy is driven by health concerns. In this paper, we present an overview on the experience of Agadir city to establish the air quality management policy, local authority on the whole have developed a good understanding of air quality in the area. Indeed for several years, efforts have been made to monitor the air quality in this city, this translated by air quality assessment since 2006 using mobile laboratory and fixed station. Our goals in this study were to review the operation of Local Air Quality Management (LAQM) making better use of available resources to improve its outcomes and make recommendations with a view to improving air quality issues. This work highlights the requirement to revise periodically the LAQM for generating priority for air quality issues within local authority and the need to implement the optimizing Air Quality Monitoring Network (AQMN).展开更多
Critical situations that cannot be solved by conventional approaches (traditional air quality monitoring networks), have the possibility of being managed quickly by a wide network of portable systems with sensors. The...Critical situations that cannot be solved by conventional approaches (traditional air quality monitoring networks), have the possibility of being managed quickly by a wide network of portable systems with sensors. The purpose of this research was to calibrate and validate low-cost sensors. Pilot indoor and outdoor areas, in the central area of Brasilia (Brazil’s capital city) were chosen for corporative performance evaluation of the sensors. The CO at 99.999% volumetric injection method has been used in a gas test box, among two MiCS-5521 (CO/VOC) sensors, one being new and the other one with a short useful life. The number of injections adopted to each volume (from 1 ml to 6 ml) was 10, rising each sensor’s confidence interval mean. A increase of the injected volume (ml) of CO resulted in significant decrease in a resistance (Ohms), as shown by a good inverse relationship on the interaction of these two variables (r = 0.88), with good measurement accuracy, when compared to the manufacturer’s reference datasheet. Finally, a geospatial management system was built for the pollution data measured by the low-cost sensors.展开更多
To solve the problem that utilizing an air rower lowers indoor air quality due to the dust stirred up in the course of rowing, an air rower is designed to have the extra function of air purifying. The designed rower i...To solve the problem that utilizing an air rower lowers indoor air quality due to the dust stirred up in the course of rowing, an air rower is designed to have the extra function of air purifying. The designed rower is composed of six parts, which are the frame, air generator, transmission part, air purifying filter cartridge, performance monitor, and electric motor. To fulfill the task, the filter cartridge is vertically arranged to lead the air to enter from the lower part and vent out of the upper part to filter and purify the air before it enters the generator so as that the indoor air is well circulated to improve the air purification effect when the rower is utilizing. The modular detachable design allows the air filter and electric motor to be installed and disassembled to adapt to different air purification needs. The designed rower has the benefit of lower purchasing cost and energy saving that may motivate exercising, bringing more fun and the sense of accomplishment.展开更多
With the atmospheric stereoscopic monitoring, air quality forecasting and decision of environment management as the main line, and comprehensive management system as the guidance, five platforms including infrastruct...With the atmospheric stereoscopic monitoring, air quality forecasting and decision of environment management as the main line, and comprehensive management system as the guidance, five platforms including infrastructure, technological support, monitoring and early monitoring, decision support and information services were established. These platforms have 15 subsystems, including stereoscopic monitoring network, visual business consultation, high-performance computing environment, comprehensive management of atmospheric data, emission inventories of pollu-tion sources, evaluation tools of atmospheric models, monitoring and management of air pollution, forecasting and early warning of air quality, diag-nostic analysis of atmospheric environment, tracking of air pollution sources, emergency management of air pollution, conformity management of air quality, comprehensive display of information, releasing of information to external networks, and releasing of information by mobile networks. The decision support system (DSS) of atmospheric environment management could realize an integration business system of 11 air quality forecast - heavy pollution weather warning - diagnosis of pollution causes (dynamic analysis of pollution sources) -air quality conformity planning (air pollu-tion emergency management) -evaluation of forecasting and warning results (evaluation pf management measures) -air quality forecasting" and provide the technical support for the prevention and control of atmosphere pollution in Anhui province.展开更多
Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby high...Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent algorithms.In this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is employed.In addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution level.For improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.展开更多
From 2007-2009, National Energy and Water Research Center (NEWRC) on behalf of Abu Dhabi Water and Electricity Authority (ADWEA) conducted a long term baseline study of nitrogen dioxide (NO2), carbon monoxide (CO), su...From 2007-2009, National Energy and Water Research Center (NEWRC) on behalf of Abu Dhabi Water and Electricity Authority (ADWEA) conducted a long term baseline study of nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and particulate matter 2, SO2, and 8 hour average of CO were within the acceptable levels of 400, 300 and 30,000 μg/m3 respectively, whereas the levels of O3 as 8 hour average (3) and PM10 (3) occasionally exceeded the FEA permissible limits during the study period. Seasonal variation based on three years data reveals that the highest concentration of NO2 and SO2 were during winter and for CO and O3 during summer months. Results indicate that the levels of SO2 and CO were significantly controlled and improved while the fuel combustion of Al Mirfa power plant had increased from 2007 to 2009. Dust has significantly impact on the air quality by elevated levels of PM10 exceed in several instances associated with regional sand- storm during the monitoring period.展开更多
Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,...Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,provides strong support for in-depth analysis of air pollution characteristics and causes.However,in the era of big data,to meet current demands for fine management of the atmospheric environment,it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality.This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period,and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas(i.e.,the“2+26”region)during the period of the three-year action plan to fight air pollution.We suggest that air quality should be comprehensively,deeply,and scientifically evaluated from the aspects of air pollution characteristics,causes,and influences of meteorological conditions and anthropogenic emissions.It is also suggested that a threeyear moving average be introduced as one of the evaluation indexes of long-term change of pollutants.Additionally,both temporal and spatial differences should be considered when removing confounding meteorological factors.展开更多
This paper focuses on ozone prediction in the atmosphere using a machine learning approach. We utilize air pollutant and meteorological variable datasets from the El Paso area to classify ozone levels as high or low. ...This paper focuses on ozone prediction in the atmosphere using a machine learning approach. We utilize air pollutant and meteorological variable datasets from the El Paso area to classify ozone levels as high or low. The LR and ANN algorithms are employed to train the datasets. The models demonstrate a remarkably high classification accuracy of 89.3% in predicting ozone levels on a given day. Evaluation metrics reveal that both the ANN and LR models exhibit accuracies of 89.3% and 88.4%, respectively. Additionally, the AUC values for both models are comparable, with the ANN achieving 95.4% and the LR obtaining 95.2%. The lower the cross-entropy loss (log loss), the higher the model’s accuracy or performance. Our ANN model yields a log loss of 3.74, while the LR model shows a log loss of 6.03. The prediction time for the ANN model is approximately 0.00 seconds, whereas the LR model takes 0.02 seconds. Our odds ratio analysis indicates that features such as “Solar radiation”, “Std. Dev. Wind Direction”, “outdoor temperature”, “dew point temperature”, and “PM10” contribute to high ozone levels in El Paso, Texas. Based on metrics such as accuracy, error rate, log loss, and prediction time, the ANN model proves to be faster and more suitable for ozone classification in the El Paso, Texas area.展开更多
文摘In a local context, sustainable development entails utilizing the current resources—material and immaterial, measurable and immeasurable, popular and unpopular—of the community in a manner that avoids overexploitation and ensures intergenerational equity. This approach prioritizes the safety and health of local citizens, placing communal productivity above corporate profitability. This research aims to assess air quality surrounding 28 chemical industry sites in Baton Rouge, Louisiana, to understand the environmental and health impacts of industrial pollutants, with a focus on environmental justice. Air quality pollutants, including PM2.5, PM10, O3, NO2, CO, and SO2, were monitored for 75 days during the Summer, using the BreezoMeter app. Python, Mapize, and QGIS software technologies were utilized for data analysis and visualization. Findings indicate a reduction in NO2 and CO levels, compared to existing literature. However, the persistent challenge of particulate matter suggests areas for further environmental management efforts. Additionally, the research suggests a significant disparity in air pollution exposure, probably affecting marginalized communities. Although the nature of the study might not fully capture annual pollution trends, the findings highlight the urgent need for the chemical industry to adopt efficient production methods and for policymakers to enhance air quality standards and enforcement, particularly in pollution-sensitive areas. The disproportionate impact of air pollution on vulnerable communities calls for a more inclusive approach to environmental justice, ensuring equitable distribution of clean air benefits and community involvement in pollution management decisions.
文摘In the state of Sao Paulo, Brazil, public policies regarding the air quality aimed at the welfare of the population are strongly dependent on monitoring conducted by the Sao Paulo State Environmental Company (CETESB), which can be influenced by faulty monitors and equipment support and cuts in power supply, among others. A research conducted from 1998 to 2008 indicated that a significant portion of the air quality automatic stations in the state of Sao Paulo did not meet the criterion of representativeness of measurements of PM10, NO2, O3, CO and SO2 concentrations which resulted in the classification of some municipalities as the nonattainment area, a situation evidenced for PM10 and O3 parameters. The network unavailability for each parameter was estimated and compared with the monitoring networks operated in Canada and the UK. This paper discusses the implications of the lack of representativeness of measurements in the environmental licensing process of pollution sources from 2008, when by the effect of state law, municipalities have been qualified according to their air quality nonattainment level.
文摘A monitoring campaign of BTEX (benzene, toluene, ethylbenzene, o- m- and p-xylene) was carried out nearby two tunnel portals in the urban area of Naples with the aim to verify air quality in this kind of urban sites. Sampling was carried out using the active adsorption technique. Sampling time was 1 h. Ambient temperature and traffic flow measurements were carried out during each sampling operation. The results indicate that average benzene concentrations at both sites exceed the limit value of 10 μg/Nm^3 established by the European Community (EC) (Dir. 2000/69). Concentration levels of other BTEX are relatively high as well. A correlation between BTEX concentration and two wheeler vehicle flow was observed.
文摘This paper proposes a simple method of optimizing Air Quality Monitoring Network (AQMN) using Geographical Information System (GIS), interpolation techniques and historical data. Existing air quality stations are systematically eliminated and the missing data are filled in using the most appropriate interpolation technique. The interpolated data are then compared with the observed data. Pre-defined performance measures root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r) were used to check the accuracy of the interpolated data. An algorithm was developed in GIS environment and the process was simulated for several sets of measurements conducted in different locations in Riyadh, Saudi Arabia. This methodology proves to be useful to the decision makers to find optimal numbers of stations that are needed without compromising the coverage of the concentrations across the study area.
文摘Air pollution is a major global issue with widely known harmful effects on human health and the environment. This pollution is a very complex phenomenon given the diversity of pollutants that may be present in the atmosphere. The air quality in urban areas is of a great concern for residents living in cities and represents a current issue that requires an adequate management. So that air quality policy is driven by health concerns. In this paper, we present an overview on the experience of Agadir city to establish the air quality management policy, local authority on the whole have developed a good understanding of air quality in the area. Indeed for several years, efforts have been made to monitor the air quality in this city, this translated by air quality assessment since 2006 using mobile laboratory and fixed station. Our goals in this study were to review the operation of Local Air Quality Management (LAQM) making better use of available resources to improve its outcomes and make recommendations with a view to improving air quality issues. This work highlights the requirement to revise periodically the LAQM for generating priority for air quality issues within local authority and the need to implement the optimizing Air Quality Monitoring Network (AQMN).
文摘Critical situations that cannot be solved by conventional approaches (traditional air quality monitoring networks), have the possibility of being managed quickly by a wide network of portable systems with sensors. The purpose of this research was to calibrate and validate low-cost sensors. Pilot indoor and outdoor areas, in the central area of Brasilia (Brazil’s capital city) were chosen for corporative performance evaluation of the sensors. The CO at 99.999% volumetric injection method has been used in a gas test box, among two MiCS-5521 (CO/VOC) sensors, one being new and the other one with a short useful life. The number of injections adopted to each volume (from 1 ml to 6 ml) was 10, rising each sensor’s confidence interval mean. A increase of the injected volume (ml) of CO resulted in significant decrease in a resistance (Ohms), as shown by a good inverse relationship on the interaction of these two variables (r = 0.88), with good measurement accuracy, when compared to the manufacturer’s reference datasheet. Finally, a geospatial management system was built for the pollution data measured by the low-cost sensors.
文摘To solve the problem that utilizing an air rower lowers indoor air quality due to the dust stirred up in the course of rowing, an air rower is designed to have the extra function of air purifying. The designed rower is composed of six parts, which are the frame, air generator, transmission part, air purifying filter cartridge, performance monitor, and electric motor. To fulfill the task, the filter cartridge is vertically arranged to lead the air to enter from the lower part and vent out of the upper part to filter and purify the air before it enters the generator so as that the indoor air is well circulated to improve the air purification effect when the rower is utilizing. The modular detachable design allows the air filter and electric motor to be installed and disassembled to adapt to different air purification needs. The designed rower has the benefit of lower purchasing cost and energy saving that may motivate exercising, bringing more fun and the sense of accomplishment.
基金Supported by the National Science and Technology Support Plan(2014BAC22B06)Public Welfare Research Project of Science and Technology Department of Anhui Province in 2017(1704f0804056)
文摘With the atmospheric stereoscopic monitoring, air quality forecasting and decision of environment management as the main line, and comprehensive management system as the guidance, five platforms including infrastructure, technological support, monitoring and early monitoring, decision support and information services were established. These platforms have 15 subsystems, including stereoscopic monitoring network, visual business consultation, high-performance computing environment, comprehensive management of atmospheric data, emission inventories of pollu-tion sources, evaluation tools of atmospheric models, monitoring and management of air pollution, forecasting and early warning of air quality, diag-nostic analysis of atmospheric environment, tracking of air pollution sources, emergency management of air pollution, conformity management of air quality, comprehensive display of information, releasing of information to external networks, and releasing of information by mobile networks. The decision support system (DSS) of atmospheric environment management could realize an integration business system of 11 air quality forecast - heavy pollution weather warning - diagnosis of pollution causes (dynamic analysis of pollution sources) -air quality conformity planning (air pollu-tion emergency management) -evaluation of forecasting and warning results (evaluation pf management measures) -air quality forecasting" and provide the technical support for the prevention and control of atmosphere pollution in Anhui province.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP2/45/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R135)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4270206DSR02).
文摘Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent algorithms.In this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is employed.In addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution level.For improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.
文摘From 2007-2009, National Energy and Water Research Center (NEWRC) on behalf of Abu Dhabi Water and Electricity Authority (ADWEA) conducted a long term baseline study of nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and particulate matter 2, SO2, and 8 hour average of CO were within the acceptable levels of 400, 300 and 30,000 μg/m3 respectively, whereas the levels of O3 as 8 hour average (3) and PM10 (3) occasionally exceeded the FEA permissible limits during the study period. Seasonal variation based on three years data reveals that the highest concentration of NO2 and SO2 were during winter and for CO and O3 during summer months. Results indicate that the levels of SO2 and CO were significantly controlled and improved while the fuel combustion of Al Mirfa power plant had increased from 2007 to 2009. Dust has significantly impact on the air quality by elevated levels of PM10 exceed in several instances associated with regional sand- storm during the monitoring period.
基金supported by the National Key Research and Development Program of China(No.2019YFC0214800)。
文摘Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,provides strong support for in-depth analysis of air pollution characteristics and causes.However,in the era of big data,to meet current demands for fine management of the atmospheric environment,it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality.This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period,and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas(i.e.,the“2+26”region)during the period of the three-year action plan to fight air pollution.We suggest that air quality should be comprehensively,deeply,and scientifically evaluated from the aspects of air pollution characteristics,causes,and influences of meteorological conditions and anthropogenic emissions.It is also suggested that a threeyear moving average be introduced as one of the evaluation indexes of long-term change of pollutants.Additionally,both temporal and spatial differences should be considered when removing confounding meteorological factors.
文摘This paper focuses on ozone prediction in the atmosphere using a machine learning approach. We utilize air pollutant and meteorological variable datasets from the El Paso area to classify ozone levels as high or low. The LR and ANN algorithms are employed to train the datasets. The models demonstrate a remarkably high classification accuracy of 89.3% in predicting ozone levels on a given day. Evaluation metrics reveal that both the ANN and LR models exhibit accuracies of 89.3% and 88.4%, respectively. Additionally, the AUC values for both models are comparable, with the ANN achieving 95.4% and the LR obtaining 95.2%. The lower the cross-entropy loss (log loss), the higher the model’s accuracy or performance. Our ANN model yields a log loss of 3.74, while the LR model shows a log loss of 6.03. The prediction time for the ANN model is approximately 0.00 seconds, whereas the LR model takes 0.02 seconds. Our odds ratio analysis indicates that features such as “Solar radiation”, “Std. Dev. Wind Direction”, “outdoor temperature”, “dew point temperature”, and “PM10” contribute to high ozone levels in El Paso, Texas. Based on metrics such as accuracy, error rate, log loss, and prediction time, the ANN model proves to be faster and more suitable for ozone classification in the El Paso, Texas area.