Executive Summary

Monitoring air purity is a critical task for government agencies in industrial and urban regions, directly impacting the achievement of Sustainable Development Goal 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities). However, the high financial investment required for comprehensive monitoring systems and the declining performance of sensors over time present significant barriers. Inaccurate measurements of air pollution compromise public health and impede progress toward sustainable urban environments. To address these challenges, this report details a proposed deep learning network for effective air quality prediction and assessment. The framework introduces an innovative methodology for weighted feature selection using an Improved Gannet Optimization Algorithm (IGOA), ensuring that the most impactful data is prioritized. Subsequent classification is performed by an Adaptive Residual Bi-LSTM network combined with Pyramid Dilation (ARBi-LSTM-PD), a model designed to identify complex patterns in environmental data. This approach represents a significant technological innovation, aligning with SDG 9 (Industry, Innovation, and Infrastructure). The model’s parameters are optimized using the IGOA strategy to maximize efficacy. The proposed model achieved a 95.175% accuracy and 87.2% precision rate, demonstrating superior performance over traditional models and offering a robust tool for safeguarding public health and advancing urban sustainability.

1.0 Introduction: Air Quality and the Sustainable Development Agenda

1.1 The Challenge to Sustainable Development Goals (SDGs)

The forecasting of real-time air quality is fundamental to mitigating the adverse effects of air pollution, a major obstacle to achieving global sustainability targets. Poor air quality directly threatens several Sustainable Development Goals:

• SDG 3: Good Health and Well-being: Air pollution is a leading cause of respiratory illnesses and premature death. Providing reliable and precise air quality estimates is essential for public health advisories and preventative action.
• SDG 11: Sustainable Cities and Communities: The viability of sustainable cities depends on managing environmental quality. High levels of pollutants like Sulphur dioxide (SO2) and Particulate Matter (PM) degrade urban living conditions and challenge the goal of making cities inclusive, safe, resilient, and sustainable.

Traditional monitoring systems face significant challenges, including sensor degradation, the need for large historical datasets for training, and the computational resources required to process extensive data. These issues limit the ability of authorities to make timely, data-driven decisions to protect citizens and the environment.

1.2 An Innovative Framework for SDG Alignment

To overcome existing limitations, an advanced deep learning framework is proposed. This model is designed to enhance the precision of air quality forecasts by integrating optimal weighted feature selection with a sophisticated classification network. This technological advancement aligns with SDG 9 (Industry, Innovation, and Infrastructure) by creating resilient and advanced technological solutions for environmental monitoring. The primary contributions of this framework are:

1. Enhanced Predictive Performance: An optimal weighted feature selection process, driven by an Improved Gannet Optimization Algorithm (IGOA), filters out less significant features to create a more efficient and interpretable model.
2. Advanced Classification: An Adaptive Residual Bi-LSTM with Pyramid Dilation (ARBi-LSTM-PD) model is developed to proficiently capture long-term dependencies and contextual information from historical data, leading to more reliable predictions.
3. Integrated Optimization: The IGOA is uniquely applied to both the feature selection and the classification parameter tuning processes, ensuring a cohesively optimized framework that can accurately assess air quality and associated health risks.

By providing more dependable forecasts, this model empowers decision-makers to implement impactful measures that reduce pollution, thereby making direct contributions to SDG 3 and SDG 11.

2.0 Methodology: A Deep Learning Framework for Air Quality Assessment

2.1 Requirement for Air Quality Prediction in Smart Cities

The development of smart cities, while improving quality of life, often leads to increased population density and transportation, which in turn elevates carbon dioxide emissions and degrades air quality. This trend directly conflicts with the objectives of SDG 11 (Sustainable Cities and Communities). An effective air quality prediction and health assessment system is therefore not just beneficial but essential for sustainable urban development, allowing for proactive management of environmental health risks and supporting the well-being of citizens as outlined in SDG 3.

2.2 Proposed Air Quality Prediction and Health Assessment Scheme

The proposed framework is designed to provide actionable intelligence for managing environmental health. The process is structured to align with sustainability objectives:

1. Data Collection: Raw data, including environmental parameters like temperature, humidity, and pollutant concentrations, are collected from established air quality datasets.
2. Optimal Weighted Feature Selection (IGOA): The IGOA, an advanced optimization algorithm, analyzes the input data to select the most relevant features and assign weights corresponding to their influence. This step ensures the model focuses on the most critical predictors, enhancing efficiency and accuracy.
3. Air Quality Index (AQI) Prediction (ARBi-LSTM-PD): The selected weighted features are fed into the ARBi-LSTM-PD network. This model processes data bidirectionally to capture temporal relationships effectively. The inclusion of residual connections mitigates the vanishing gradient problem, while Pyramid Dilation expands the network’s receptive field without increasing computational load.
4. Parameter Optimization: The IGOA is again utilized to optimize the parameters of the RBi-LSTM network, further boosting the accuracy of the AQI prediction.
5. Health Assessment: The predicted AQI value is used to classify the air purity level into categories (e.g., Good, Moderate, Poor), which directly correspond to potential health impacts. This provides a clear link between environmental conditions and public health outcomes, supporting SDG 3.

3.0 Results and Performance Evaluation

3.1 Model Efficacy and Validation

The proposed IGOA-ARBi-LSTM-PD model was rigorously evaluated against established machine learning and optimization algorithms, including KNN, XGBOOST, SVM, and traditional GOA. The performance was measured across three distinct datasets to ensure robustness and generalizability. The key findings demonstrate a significant advancement in predictive capability:

• Superior Accuracy: The model achieved an overall accuracy of 95.175% and a precision of 87.2%, outperforming all baseline models.
• Enhanced Convergence: The convergence analysis showed that the proposed model required a lower cost function value to achieve optimal results compared to other heuristic-assisted models, indicating greater efficiency.
• Robustness: The model maintained high performance across different K-fold validation tests and activation functions, confirming its stability and reliability.

3.2 Implications for Public Health and Sustainable Urban Policy

The high accuracy of the model has direct and practical implications for advancing the Sustainable Development Goals:

• Supporting SDG 3 (Good Health and Well-being): The model’s reliability enables public health authorities to issue timely and accurate warnings during periods of high pollution. This allows vulnerable populations to take necessary precautions, reducing exposure and mitigating health risks associated with poor air quality.
• Advancing SDG 11 (Sustainable Cities and Communities): For urban planners and environmental agencies, the model serves as a powerful tool for data-driven policy-making. It can be used to assess the impact of traffic management strategies, industrial regulations, and green infrastructure projects on air quality, helping to reduce the adverse environmental impact of cities.

4.0 Conclusion and Recommendations

This report details an effective air quality prediction and health assessment system that leverages an IGOA-optimized ARBi-LSTM-PD network. By integrating optimal weighted feature selection with an advanced deep learning classifier, the framework achieves state-of-the-art accuracy in forecasting the Air Quality Index. This technological innovation provides a robust solution to the challenges of conventional air quality monitoring.

The model’s success in delivering timely and trustworthy air quality evaluations is essential for public health initiatives and sustainable urban management. The direct link between its predictive output and health impact assessment provides a clear mechanism for advancing SDG 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities). The advanced predictive capability allows policymakers to move from a reactive to a proactive stance on air pollution, protecting citizens and fostering healthier, more sustainable environments.

Recommendations for Implementation

1. Integration with Public Health Systems: Government health agencies should integrate this predictive model into public alert systems to disseminate timely health advisories, directly supporting the targets of SDG 3.
2. Application in Urban Planning: Municipal authorities and environmental agencies should utilize the model’s outputs for dynamic traffic management, industrial zoning, and evaluating the effectiveness of pollution control policies to build more sustainable cities in line with SDG 11.
3. Fostering Further Innovation: Continued investment in advanced predictive analytics and AI-driven environmental monitoring should be encouraged to enhance model scalability and address emerging environmental challenges, contributing to SDG 9.

Analysis of Sustainable Development Goals (SDGs) in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

1. SDG 3: Good Health and Well-being

   The article directly connects air quality to public health. It states that air pollution leads to “severe health issues, which cause a high death rate,” including “respiratory issues and lung disease.” The proposed system aims to “reduce the health hazards associated with it” and “evaluates health risks linked to different levels of air pollution,” which aligns with ensuring healthy lives and promoting well-being.

2. SDG 9: Industry, Innovation, and Infrastructure

   The core of the article is the development of an innovative technological solution—a deep learning network (ARBi-LSTM-PD) and an optimization algorithm (IGOA)—to solve a critical environmental monitoring problem. This focus on creating advanced technology and improving data analysis systems for industrial and urban monitoring directly supports the goal of fostering innovation and upgrading technological capabilities.

3. SDG 11: Sustainable Cities and Communities

   The article explicitly frames the problem within the context of “industrial and urban regions” and “smart cities.” It highlights that monitoring air purity is a “crucial task for government agencies” in these areas. By developing a system to predict and assess air quality, the research contributes to making cities and human settlements more inclusive, safe, resilient, and sustainable by addressing urban environmental challenges.

4. SDG 13: Climate Action

   While not the primary focus, air pollution and climate change are intrinsically linked. The pollutants monitored by the system, such as those contributing to the Air Quality Index (AQI), often share sources with greenhouse gases. Improving air quality monitoring and prediction provides crucial data that can inform policies aimed at mitigating climate change and its impacts, and the system can serve as an early warning for pollution events.

2. What specific targets under those SDGs can be identified based on the article’s content?

• Target 3.9: Substantially reduce deaths and illnesses from pollution

  The article’s motivation is to create a system that helps people “take precautions to avoid air pollution and reduce the health hazards associated with it.” The health assessment component, which links AQI values to health impacts, directly addresses the goal of reducing illnesses and deaths from air pollution.

• Target 9.5: Enhance scientific research and upgrade technology

  The entire paper is a contribution to scientific research. It proposes a novel methodology (“Improved Gannet Optimization Algorithm,” “Adaptive Residual Bi-LSTM network”) to enhance the performance of air quality prediction, which is a direct effort to “upgrade the technological capabilities of industrial sectors.”

• Target 11.6: Reduce the environmental impact of cities

  The article’s central theme is the development of a tool for “monitoring and safeguarding the air’s purity” in “industrial and urban regions.” This directly supports the target of reducing the adverse per capita environmental impact of cities by paying special attention to air quality.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Indicator 3.9.1: Mortality rate attributed to household and ambient air pollution

  The article implies this indicator by focusing on assessing “health risks linked to different levels of air pollution” and aiming to “reduce the health hazards” that cause a “high death rate.” The system’s output (AQI levels and associated health warnings) is a tool to manage exposure and ultimately reduce this mortality rate.

• Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g., PM2.5) in cities

  The article explicitly mentions that the data captured by air quality systems includes “Particulate Matter (PM).” The Air Quality Index (AQI) that the system predicts is a composite measure based on the concentration of PM and other pollutants. Therefore, the system is designed to monitor and predict the very substances measured by this indicator.

• Implied Technological Indicators (for Target 9.5)

  The article provides performance metrics for its proposed model, such as an “accuracy and precision rate of… 95.175% and 87.2%.” These metrics serve as indicators of technological advancement and improved capability in the field of environmental monitoring, demonstrating progress in scientific and innovative capacity.

4. Table of SDGs, Targets, and Indicators

Source: nature.com