This article presents a location-invariant air pollution prediction model with good geographic generalizability. The model uses a light GBR as part of a machine-learning framework to capture the spatial identification of air contaminants. Given the dynamic nature of air pollution, the model also uses a random forest to capture temporal dependencies in the data. Our model uses a transfer learning strategy to deal with location variability. The algorithm can learn concentration patterns because it has been trained on a vast data set of air quality measurements from various locations. The trained model is then improved using information from a particular target site, customizing it to the features of the target area. Experiments are carried out on a comprehensive data set containing air pollution measurements from various places to assess the efficacy of the proposed model. The recommended method performs better than standard models at forecasting air pollution levels, proving its dependability in various geographical settings. An interpretability analysis is also performed to learn about the variables affecting air pollution levels. We identify the geographical patterns associated with high-pollutant concentrations by visualizing the learned representations within the model, giving important information for environmental planning and mitigation methods. The observations show that the model outperforms state-of-the-art forecasting based on recurrent neural network and transformer-based models. The suggested methodology for forecasting air contaminants has the potential to improve air quality management and aid in decision-making across numerous regions. This helps safeguard the environment and public health by creating more precise and dependable air pollution forecast systems.

The detrimental effects on human health caused by air pollution show that being able to predict air quality is a task of utmost significance. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) is seen as promising in this domain. The performances of state-of-the-art models in terms of prediction accuracy vary with different pollutants and are acceptable only for certain pollutants only. This paper uses Machine Learning (ML) and Deep Learning (DL) models to predict the concentrations of six major air pollutants. Data is collected over 8 months with 1400 daily instances from sensors deployed in Kuala Lumpur, Malaysia. As an intelligibly robust system, in this paper a hybrid ensemble model using a combination of ML models, specifically Random Forest, K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGBoost), and Neural Network models, namely Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), and Convolutional Neural Networks (CNN). Here, a hybrid ensemble learning model is created using five various ML models as weak learners. In previous ensemble models, a homogeneous group of weak learners is utilized; however, this work uses a heterogeneous group of weak learners. The prediction accuracy is compared using R2 score, absolute, squared, and root mean squared errors.

Poor air quality has been identified as one of the main risks to human health, especially in developing regions, where the information on physical chemical properties of air pollutants is lacking. To bridge this gap, we conducted an intensive measurement campaign in Manila, Philippines to determine the emission factors (EFs) of particle number (PN) and equivalent black carbon (BC). The focus was on public utility jeepneys (PUJ), equipped with old technology diesel engines, widely used for public transportation. The EFs were determined by aerosol physical measurements, fleet information, and modeled dilution using the Operational Street Pollution Model (OSPM). The results show that average vehicle EFs of PN and BC in Manila is up to two orders of magnitude higher than European emission standards. Furthermore, a PUJ emits up to seven times more than a light-duty vehicles (LDVs) and contribute to more than 60% of BC emission in Manila. Unfortunately, traffic restrictions for heavy-duty vehicles do not apply to PUJs. The results presented in this work provide a framework to help support targeted traffic interventions to improve urban air quality not only in Manila, but also in other countries with a similar fleet composed of old-technology vehicles.

Air pollution poses a significant threat to urban environments, and accurate prediction of multiple air pollutants is crucial for effective mitigation strategies. This study introduces a novel time-aware auto-regressive long-short-term memory (TAR LSTM) approach to address this challenge by developing a multivariate prediction model using artificial intelligence (AI) for SMART city applications. Existing models often fall short of predicting all six major criteria pollutants comprehensively. In response, this work proposes an autoregressive (AR) neural network model based on the long short-term memory (LSTM) architecture, which excels in capturing temporal dependencies within sequential data. The proposed method uses the AR model that captures the linear dependencies in the time series, while the LSTM captures the nonlinear dependencies and long-term patterns. This enables the model to consider past pollutant concentrations and their relationships, resulting in a more accurate and dynamic prediction. Rigorous testing on datasets from low-cost air quality sensors (LAQSs) validates the model’s superior performance. Datasets from diverse locations, including India, Malaysia, and the Philippines, contribute to the robustness of the model, showcasing its efficacy in varied urban environments. This research contributes to advancing predictive modeling for air quality, addressing the limitations of previous approaches, and providing a promising solution for SMART city implementations. The findings highlight the AR LSTM model’s potential as a valuable tool for precise and comprehensive air pollution forecasting, which has implications for informed decision making and better urban environmental management.

Diarrhoea, malnutrition, and dehydration threaten the lives of millions of children globally due to inadequate water, sanitation, and hygiene (WaSH). Our study aimed to identify environmental and behavioural risk factors of these health outcomes among schoolchildren in Metro Manila, Philippines. We analysed data from a multistage cluster sample of schoolchildren in grades 5, 6, 7, 9, and 10 (ages ~10-15 years old) to investigate WaSH facilities and hygiene practices. Outcomes were: self-reported diarrhoea, measured via questionnaire; observed malnutrition (stunting, undernutrition [underweight/thin and wasted/severely thin], over-nutrition [overweight and obese]), measured via anthropometry; dehydration, measured via urine specific gravity/urine test strips. We used multiple logistic regression to explore correlates. We included 1558 students from 15 schools in three cities. Over 28% (421) of students had diarrhoea and 68% (956) were dehydrated. Over 15% (227) of students were stunted, ~9% (127) were undernourished, and >21% (321) were over-nourished. Diarrhoea was associated with poor handwashing, while dehydration was associated with the lack of water in school restrooms. Stunting was linked with not using the school restroom, the lack of water in school restrooms, and the lack of hygiene lessons in school. Undernutrition was associated with the lack of a school restroom cleaning policy. Risks of diarrhoea, stunting, and undernutrition decreased as the number of school restrooms increased. Risks of stunting and overnutrition decreased as the numbers of school toilets increased. Having more than seven handwashing basins was associated with decreased risk of dehydration. Findings from our cross-sectional study cannot describe causation. We have found associations that suggest that school restroom cleaning policies, adequate water supply, improved handwashing, and hygiene education are needed to prevent disease. School-based WaSH interventions are recommended to provide water in school WaSH facilities, promote handwashing, and improve hygiene-related knowledge.

Accurate and reliable air quality forecasting is essential for protecting public health, sustainable development, pollution control, and enhanced urban planning. This letter proposes a novel architecture namely WaveCatoost to forecast the real-time concentrations of air pollutants by combining the maximal overlapping discrete wavelet transform (MODWT) with the CatBoost model. This hybrid approach efficiently transforms time series of air pollution concentration levels into high-frequency and low-frequency components, thereby extracting signal from noise and improving prediction accuracy and robustness. Evaluation of two distinct regional datasets, from the Central Air Pollution Control Board (CPCB) sensor network and a low-cost air quality sensor system (LAQS), underscores the superior performance of our proposed methodology in real-time forecasting compared to the state-of-the-art machine learning and deep learning architectures.