Air Quality Classification Research Articles

Evaluation of the Decision Tree Model for Air Condition Classification on the Global Air Pollution Dataset

Air pollution is an urgent global environmental problem, with significant impacts on public health and ecosystem stability. This research aims to develop an air quality classification model using the Global Air Pollution dataset from Kaggle, which consists of 23,463 rows of data and 12 features, including important variables such as Air Quality Index (AQI), PM2.5, NO2, and O3. Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms are applied to perform classification, with a focus on hyperparameter tuning to increase model accuracy. The research results show that the Decision Tree provides the best results with an accuracy of 99.89% after tuning hyperparameters using the Grid Search method. The SVM model showed an improvement of 94.89% to 99.32%, while Random Forest recorded an accuracy of 96.87% with no significant improvement after tuning. Importance feature analysis identified PM2.5 and AQI as the dominant factors in influencing air quality, with PM2.5 having the highest importance value of 0.93. This research confirms that machine learning can be an effective tool for integrating and classifying air pollution. It is hoped that the integration of this model into a real-time air quality monitoring system can help make more responsive and precise decisions in dealing with air pollution problems.

Multi-classification prediction of PM2.5 concentration based on improved adaptive boosting rotation forest

Developing efficient, stable, and user-friendly methods and technologies for predicting air quality has contributed to environmental research and management. Most traditional machine learning (ML) models often struggle to efficiently process extensive air quality data and grapple with the challenge of imbalanced data distributions. To this end, we introduced a novel multi-strategy collaborative approach that incorporates weighted feature selection, an adaptive enhanced rotation forest algorithm, and Bayesian Optimization for parameter tuning. Moreover, to improve the transparency in black box ML models, the novel Shapley Additive Explanations (SHAP) method was applied to interpret the outputs and analyze the importance of individual variables. Through experiments on real air quality datasets, we have verified the accuracy of our proposed method in classifying air quality levels. Notably, our model demonstrated an average test set accuracy improvement of approximately 10 % in cities like Beijing, Tianjin, Shijiazhuang, and Baoding after hyperparameter optimization using Bayesian methods. Furthermore, compared to alternative algorithms, our model showed an improvement of 1–2 % in evaluation metrics. By introducing novel methodologies and tools, our research contributes significantly to the advancement of air quality classification technology and holds profound implications for informing environmental policy decisions.

Diffusion-driven Incomplete Multimodal Learning for Air Quality Prediction

Predicting air quality using multimodal data is crucial to comprehensively capture the diverse factors influencing atmospheric conditions. Therefore, this study introduces a multimodal learning framework that integrates outdoor images with traditional ground-based observations to improve the accuracy and reliability of air quality predictions. However, aligning and fusing these heterogeneous data sources pose a formidable challenge, further exacerbated by pervasive data incompleteness issues in practice. In this paper, we propose a novel incomplete multimodal learning approach (iMMAir) to recovery missing data for robust air quality prediction. Specifically, we first design a shallow feature extractor to capture modal-specific features within the latent representation space. Then we develop a conditional diffusion-driven recovery module to mitigate the distribution gap between the recovered and true data. This module further incorporates two conditional constraints of temporal correlation and semantic consistency for effective modal completion. Finally, we reconstruct incomplete modalities and fuse available data using a multimodal transformer network to predict the air quality. To alleviate the modality imbalance problem, we employ an adaptive gradient modulation strategy to adjust the optimization of each modality. Experimental results demonstrate that iMMAir significantly reduces prediction errors, outperforming baseline models by an average of 5.6% and 2.5% in air quality regression and classification tasks. Our source code and data are available at https://github.com/pestasu/IMMAir.

Enhancing air quality classification using a novel discrete learning-based multilayer perceptron model (DMLP)

Enhancing air quality classification using a novel discrete learning-based multilayer perceptron model (DMLP)

Wireless sensor network-based machine learning framework for smart cities in intelligent waste management

Environmental safety is one of the key issues that are directly related to a country's prosperity. One of the most fundamental aspects of a sustainable economy is waste management and recycling. Better recycling safety and efficiency may be achieved via the use of intelligent devices rather than manual effort. In this research, we describe a machine learning-based architecture for smart trash collection and sorting using the Internet of Things and wireless sensor networks. The goal of this study was to develop an autonomous method for producing an efficient and intelligent waste parameter monitoring system for a novel waste management system, using the Internet of Things (IoT) and Long Range (LoRa) technologies. Several possibilities are explored, all of which may be applied to the development of the three nodes. The number of trash cans, garbage stench, air quality, weight, smoke levels, and waste categories are all tracked in real-time via the Internet of Things and the Thing Speak Cloud Platform, which can be set up in numerous places. In the end, a fog layer-deployed intelligent waste classification framework consists mostly of four layers: input, feature, classification, and output. Using the Thrash Box dataset, the proposed system develops a categorization method into trash classes such as household, medical, and electronic garbage, in addition to object identification. Traditional machine learning methods, such as the multi-kernel support vector machine (SVM) and the Adaboost ensemble classifier, are employed in the classification layer, while the Resnet-101 deep convolutional neural network model is used in the feature layer. Experiments were conducted to evaluate the suggested method's ability to classify garbage and provide accurate predictions about their respective categories. Compared to other state-of-the-art models, the suggested method's performance was shown to be superior in the presented trials.

Impact of ventilation system retrofitting on indoor air quality in a single-family building

Due to prolonged human occupancy within indoor environments, the quality of indoor air holds significant importance. This study evaluates the impact of retrofitting a ventilation system from natural to mechanical on indoor air quality (IAQ) in a bedroom during heating seasons in 2017/18 and 2020/21. Measurements were conducted before and after modernization to assess CO2 concentrations, temperature, humidity, particulate matter (PM) and bacterial and fungal aerosols levels indoors and outdoors. Results show a significant decrease in CO2 concentration (from 1000 ± 240 ppm to 806 ± 165 ppm) and relative humidity (from 53.0 ± 3.6 % to 28.2 ± 4.1 %) post-modernization. Notable reduction in CO2 concentrations averaging 20 % was recorded, thereby elevating air quality category from IDAIII to IDAII. Additionally, the study determined the infiltration coefficient for PM2.5, which increased from 0.48 under natural ventilation to 0.63 after retrofitting. PM2.5 concentrations increased indoors (from 23.49 ± 11.21 μg/m3 to 56.68 ± 13.47 μg/m3) due to higher infiltration rates with mechanical ventilation. Bioaerosol measurements indicate fluctuating concentrations, with bacterial aerosols peaking in the evening. The use of mechanical ventilation led to increased transport and dispersion of bacterial bioaerosols indoors. In contrast, this was not observed for fungal aerosols. Additionally, the study highlights limitations in ventilation filtration efficiency and recommends the installation of PM2.5 sensors to regulate airflow during periods of high outdoor PM2.5 concentrations. Overall, while mechanical ventilation improved some IAQ parameters, it posed challenges in controlling particulate matter infiltration, emphasizing the need for comprehensive ventilation strategies to ensure indoor air quality and occupants' well-being.

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region.

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

Decision Tree versus k-NN: A Performance Comparison for Air Quality Classification in Indonesia

Decision Tree versus k-NN: A Performance Comparison for Air Quality Classification in Indonesia

The development of color histogram method to identify air quality index based on sky images

Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images.

Air Quality Changes under the Influence of Cut-Off Lows and Omega Blocking Patterns and Associated Health Risks in a Coastal Mediterranean Region: Case of Bizerte, Northern Tunisia

The study of the daily and diurnal variability of NO, NO2, surface O3 and PM10 was carried out in urban Bizerte (Northern Tunisia), from January to March, 2013. The main results showed three distinct periods depending on multi-scale sources and meteorology. The first period, (≈20% of total cases) influenced by moderately stable conditions, was characterized by a slight excess of NO due to traffic emissions. The second period, (≈ 60% of total cases) associated with moderately unstable conditions, was devoid of any significant impact of sources. The third period, (≈ 20% of total cases) distinguished by noteworthy excess of O3 and PM10, was attributed to a marked effect of succeeding Cut-off lows and Omega blocking patterns. Under these conditions, the scatter plots of NO, NO2, and O3 versus NOx for daytime and nighttime showed good polynomial fit curves with high R2 values for NO and NO2 only. Under both moderately unstable and stable conditions, significant negative correlation coefficients between O3 and NOx were found to be 0.53 (p < 0.05) and 0.50 (p < 0.05) for daytime and nighttime, respectively. Under Cut-off lows and Omega, an absence of any significant correlations between O3 and NOx was noticed. Under Cut-off lows, the good linear regression fits between OX (=O3 + NO2) and NOx illustrated high NOx independent contributions, possibly attributed to the biogenic VOCs effect. The investigation of the air quality index (AQI) for NO2, PM10 and O3 (running 8-h mean) highlighted the significant effect of both O3 and PM10 only under Cut-off lows and Omega which were retraced by poor to very poor air quality categories.

Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification

&lt;p&gt;Rapid economic development, industrialization, and urbanization in Indonesia have caused a large increase in air pollution with negative impacts on the environment and public health. The aim of this research is to use machine learning techniques to categorize air quality and generate an air quality index (AQI) using a dataset that includes six prevalent air pollutants. Next steps are preprocessing and data extraction, K-nearest neighbors (KNN) classification, support vector machine (SVM), and random forest (RF) models are implemented. Furthermore, synthetic minority oversampling technique (SMOTE) is incorporated into the ensemble learning process to improve the results. This research uses K-fold cross validation for improve classification accuracy and reduce overfitting. Research findings show that the application of SMOTE brings a significant increase in model accuracy, effectively solving the problem of imbalanced data sets. These insights provide direction for effective air quality monitoring systems and informed decision making in air pollution management.&lt;/p&gt;

Explainable based approach for the air quality classification on the granular computing rule extraction technique

Air pollution corresponds to one of the considerable challenges and disastrous sides of the environment that causes severe damage to all its biodiversity, including humans. As a result, establishing efficient, reliable, and interpretable methods and techniques to predict and control air quality is a must to preserve the environment and consider the necessary precautions. Most traditional machine learning models often lack transparency, making it challenging to interpret their decisions, especially in vital domains like air pollution. This paper proposes a novel approach that leverages granular computing to extract interpretable rules for air quality classification. We demonstrate the effectiveness of our approach through experiments on a real-world air quality dataset, showcasing the interpretability of the extracted rules and their accuracy in classifying air quality levels. The output of the proposed GrC model is a tree-like structure minimizing the entropy, allowing an easier interpretation of the classification results. A comparison is conducted with some widely used machine learning algorithms, including decision tree classifier, random forest, and CatBoost. The results indicate that the proposed granular computing rule extraction approach shows a competitive performance according to traditional black-box models in terms of accuracy (79%), transparency and reliability. The developed GrC model and the findings of this study not only contribute to advancing the field of air quality classification but also bear broader implications for environmental research and management for relevant and informed decision-making.

Air quality monitoring using statistical learning models for sustainable environment

High levels of air pollutants pose significant health risks, increasing the chances of respiratory infections, lung cancer, and heart complications, particularly for those already susceptible to illness. Modern societal advancements have worsened air quality degradation, with daily activities such as transportation, industrial processes, and domestic operations releasing harmful contaminants. This study addresses the urgent need for air quality monitoring and forecasting, especially in developing nations like India, where machine learning-based prediction technologies play a crucial role in understanding environmental aspects. Our study focuses on analyzing and predicting air quality using data from two distinct areas in Kolkata—Victoria and Rabindra. Thorough pre-processing and data analysis have been conducted to identify essential features and detect undermining patterns in the data. Utilizing five classic machine learning algorithms, we predict the air quality category by categorizing the predicted AQI into six AQI classes, emphasizing extensive hyper-parameter tuning for each model. The Rabindra dataset yields the best-performing Support Vector Machine (SVC) model with a 97.98% accuracy, while the best prediction accuracy for Victoria location is 93.29% from Random Forest Classifier (RFC). This study offers valuable insights into the effectiveness of machine learning algorithms for air quality prediction, with the novelty of the study lying in the focus on hyper-parameter tuning to achieve the highest accuracy.

Implementation of the Ordinal Logistic Regression Method for Air Quality Classification Based on the Air Pollution Standard Index

Air pollution is a serious problem in many cities around the world, caused by human and natural activities. Jakarta, as an Indonesian economic and transportation hub, faces serious air pollution challenges. This study uses the Ordinal Logistics Regression Method to develop an optimal classification model for identifying air quality based on the air pollution index. The aim is to contribute to dealing with air contamination and improve understanding of the use of such methods in the classification of air quality. Data used from 2012 to 2021 covered parameters such as PM10, SO2, CO, O3, and NO2. Oversampling is done using SMOTE to address imbalances in the datasets used. The data used is divided into two parts: training data and validation test data, with an 80:20 ratio. The model training and testing process is carried out with a variety of scenarios, including parameter significance tests using probability tests and Wald tests, cross-validation with fold numbers 5, 10, and 15, as well as evaluation using a confusion matrix. Models are used for Mord libraries such as Ordinalridge, LogisticAT, logisticIT, and LogisticSE. There were a total of 72 model testing experiments to find the best model of the six data model outputs. The test results showed that the optimal model was obtained with a data deletion scenario of null values, data oversampling using the LogisticIT model, and K-fold = 5, with a training accuracy of 0.8628 and validation data test accuracy of 0.8599, as well as each precision value of 0.86, recall value of 0.86, and F1-score of 0.86. The model's performance was satisfactory in handling different data variations, according to the evaluation. These results show that the model is able to generalize data well and is reliable in predicting air quality accurately.

Enhancing deep learning model performance in air quality classification through probabilistic hyperparameter tuning with tree-structured parzen estimators

The research introduces an innovative approach to enhance deep learning models for air quality classification by integrating tree-structured Parzen estimators (TPE) into the hyperparameter tuning process. It applies this approach to CNN, LSTM, DNN, and DBN models and conducts extensive experiments using an air quality dataset, comparing it with grid search, random search, and genetic algorithm methods. The TPE algorithm consistently outperforms these methods, demonstrating improved classification accuracy and generalization. This approach’s potential extends to enriching water quality classification models, contributing to environmental sustainability and resource management. Bridging deep learning with TPE offers a promising solution for optimized air quality classification, supporting informed environmental preservation efforts.

Urban Air Quality Classification Using Machine Learning Approach to Enhance Environmental Monitoring

Urban areas worldwide grapple with environmental challenges, notably air pollution. DKI Jakarta, Indonesia's capital city, is emblematic of this struggle, where rapid urbanization contributes to increased pollutants. This study employed the CatBoost machine learning algorithm, known for its resistance to overfitting and capability to handle missing data, to predict urban air quality based on pollutant levels from 2010 to 2021. The dataset, sourced from Jakarta's air quality monitoring stations, includes pollutants such as PM10, SO2, CO, O3, and NO2. After preprocessing, we used 80% of the data for training and 20% for testing. The model displayed high accuracy (0.9781), precision (0.9722), and recall (0.9728). The feature importance chart revealed O3 (Ozone) as the top influencer of air quality predictions, followed by PM10. Our findings highlight the dominant pollutants affecting urban air quality in Jakarta, Indonesia and emphasizing the need for targeted strategies to reduce their concentrations and ensure a cleaner and healthier urban environment.

PERBANDINGAN KNN DAN SVM UNTUK KLASIFIKASI KUALITAS UDARA DI JAKARTA

The growth and economic development of a city is one of the factors causing air pollution because air quality has been mixed with various components of chemical compounds such as motor vehicle exhaust gases and factory smoke waste. Data mining is a method to find out information about air pollution in the city of Jakarta. The data mining method used is classification because this method can process air pollution standard index (AQI) parameter data into information that can show the level of air quality per day using the K-Nearest Neighbor algorithm and Support Vector Machine. &#x0D; The result of the application of data mining for air quality classification in Jakarta is that the Support Vector Machine algorithm has better accuracy performance compared to the K-Nearest Neighbor algorithm. The Support Vector Machine algorithm uses the RBF kernel and 100 kernel parameter gets an accuracy value of 98%, precission of 97%, recall of 97%, and F1-Score of 97% while the K-Nearest Neighbor algorithm uses the number of K as much as 6 gets an accuracy value of 96%, precission of 96%, recall of 93%, and F1-Score of 94%.

Status Mutu Kualitas Udara Ambien Pada Beberapa Kota Besar di Papua Menggunakan ArcGIS

The increase in population in recent years has affected the utilization of facilities and infrastructure, including the use of vehicles. For the past 10 years, the population in Papua has increased by around 4.13% per year, resulting in the need for daily mobility to increase. The high mobility demand in Papua could affect the increase in pollution, thus affecting the air quality. Exposure to pollutants in ambient air and environmental noise in the long term will have an impact on human health. Therefore, in this study, we analyze the current conditions of air pollution parameters in several major cities in Papua, such as Jayapura, Merauke, Sentani, and Timika. The method used is a survey. Environmental parameters measured were air content of SO2, NO3, PM 10, PM 2.5, HC, O3, CO, dust, Pb, noise, and The Air Pollutant Standard Index (ISPU) value. Observational data obtained were analyzed and displayed using the ArcGIS program. The parameters for determining the location of sampling points are densely packed highway areas, industrial areas, residential areas, and offices. The results of the study indicated that the ambient air quality in all study sites is below the required quality standard. While the results of environmental noise that exceed the highest quality standards located in specifics area on each study site are as follows, Merauke at PLTD Merauke I PT. PLN Persero with 81.24 dB(A), Sentani at Sentani City Square Roads with 70.2 dB (A), Mimika at Perumahan Pemda SP-2 with 65.30 dB(A), and Sentani at Kantor Bupati Jayapura with 64, 54 dB (A). However, the ISPU value for pollutant substances is still in the range of 0-50, which means that the air quality category is very good to carry any outdoor activities, and does not have any negative impact on humans, animals, or plants.Key words: air quality; noice; ISPU; Papua.

Mining association rules between lichens and air quality to support urban air quality monitoring in Nigeria

Urban environments represent the most intense human-environment interaction. This interaction can result in negative outcomes like air pollution and its health implications. There is a significant data deficit in air quality monitoring across many developing nations, which prevents effective policies and measures from being taken to promote the accomplishment of sustainable development. Around the world, lichens have been used to track environmental changes due to their sensitivity to changes and concentration of atmospheric pollutants. This study investigated the relationships between lichen and air quality across some Nigerian cities. Lichen surveys were conducted in four cities. At various periods during the day, NO2, SO2, PM2.5, and PM10 levels were measured. Association rule mining was carried out to investigate the relationship between lichen found and air quality categories. Results showed that the most prevalent lichen Genera are Pyxine in Abuja and Kano, Diorygma in Lagos, and Dirinaria in Port Harcourt. Out of the 40 rules found from the rule mining, 17 are important (lift values ≥ 1.1), capturing six of the fourteen lichen genera identified in the field. The findings indicated that there are important relationships between lichens and air quality indices, suggesting that some lichen species in Nigeria may serve as indicators of long-term air quality. To develop a network of urban environmental quality bioindicators across Nigerian cities, surveying and transplanting are advised. The use of lichen for air quality monitoring can provide information for sustainable management of air quality and environmental quality in Nigeria.

Air Quality Classification and Measurement Based on Double Output Vision Transformer

The atmospheric quality monitor is an essential component of atmospheric protection. However, using complicated large-scale chemical equipment required by traditional methods is difficult for ordinary people. We are committed to allowing ordinary people to obtain a local air quality index (AQI) and then participate in the air quality monitoring of the entire district quickly and easily. So, we propose a method improved from Transformer in this article. This method processes pictures taken by mobile devices and predicts a more accurate local AQI level. The proposed method performs better than traditional methods in frequency and flexibility. Tokens in the Double Output Vision Transformer (DOViT) proposed in this article will be processed with the multihead self-attention (MSA) mechanism of the model to automatically extract the features in pictures for achieving higher classification accuracy. The application of Transformer makes our model more concise and efficient than previous CNN-based methods.