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Air pollution’s effects on physical health, especially cardiovascular and respiratory, are well known. Exposure to air pollution may damage every organ and cell in the human body. New evidence is emerging showing that air pollution adversely affects human mental health. Current research suggests that high air pollution levels have long-term mental health effects, such as reduced mental capacity and increased cognitive decline, leading to increased stress, anxiety, and depression. Objectives: This scoping review aims to provide a comprehensive overview of the methods used in epidemiological literature to ascertain the existence of links between outdoor particulate matter (PM) and multiple adverse mental health (MH) effects (depression, anxiety, and/or stress). A better understanding of the practical research methodologies could lead to improved air quality (AQ) management and enhanced well-being strategies. Methods: This paper undertakes a scoping review. PubMed and EMBASE databases from 2010 to 2024 were searched for English-language human cohort observational studies stating methodologies used in analyzing the link between outdoor particulate matter (ultrafine (UFT) (<0.1 μm), fine (<2.5 μm), and course (<10 μm)) and mental health outcomes (depression, anxiety, and stress) in adults (>18 years), excluding vulnerable populations (i.e., elderly, children, and pregnant women). The study focuses on urban, suburban areas, and rural areas. Results: From an initial search of 3889 records, 29 studies met the inclusion criteria and were included in the review. These studies spanned various countries and employed robust quantitative methodologies to assess AQ and MH. All included studies investigated the impact of PM on mental health, with some (n = 19/65.52%) also examining nitrogen oxides (NOx), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Depression was the most frequently studied outcome (n = 10/34.48%), followed by anxiety and depression (n = 6/20.69%), and anxiety, stress, and depression, and stress (n = 4/13.79%, each). Depression, anxiety, and stress together were examined in a single study (n = 1/3.45%). Standardized questionnaires involving psychological scales such as Patient Health Questionnaire (PHQ) (n = 7/24.14%) and The Center for Epidemiological Studies-Depression (CES-D) (n = 3/10.34%) for depression and Generalized Anxiety Disorder Questionnaire (GAD) (n = 2/6.90%) for anxiety were commonly used MH tools. 27 out of 29 studies found a significant negative impact of air pollution on mental health, demonstrating a solid consensus in the literature. Two studies did not find a significant correlation. The results consistently indicated that higher levels of air pollution were associated with increased symptoms of depression, anxiety, and stress. Conclusion: Of the 3889 identified studies, 29 were suitable for inclusion in the scoping review per inclusion criteria. The results show the most preferred methods in assessing air quality and mental health in relevant studies, providing a detailed account of each method’s strengths and limitations used in studies. This scoping review was conducted to assist future research and relieve the decision-making process for researchers aiming to find a correlation between air quality and mental health. While the inclusion criteria were strict and thus resulted in few studies, the review found a gap in the literature concerning the general adult population, as most studies focused on vulnerable populations. Further exploration of the methodologies used to find the relationship between air quality and mental health is needed, as reporting on these outcomes was limited.
Accurate long-term PM2.5 prediction is crucial for environmental management and public health. However, previous studies have mainly focused on short-term air quality point predictions, neglecting the importance of accurately predicting the long-term trends of PM2.5 and studying the uncertainty of PM2.5 concentration changes. The traditional approaches have limitations in capturing nonlinear relationships and complex dynamic patterns in time series, and they often overlook the credibility of prediction results in practical applications. Therefore, there is still much room for improvement in long-term prediction of PM2.5. This study proposes a novel long-term point and interval prediction framework for urban air quality based on multi-source spatial and temporal data, which further quantifies the uncertainty and volatility of the prediction based on the accurate PM2.5 point prediction. In this model, firstly, multi-source datasets from multiple monitoring stations are preprocessed. Subsequently, spatial clustering of stations based on POI data is performed to filter out strongly correlated stations, and feature selection is performed to eliminate redundant features. In this paper, the ConvFormer-KDE model is presented, whereby local patterns and short-term dependencies among multivariate variables are mined through a convolutional neural network (CNN), long-term dependencies among time-series data are extracted using the Transformer model, and a direct multi-output strategy is employed to realize the long-term point prediction of PM2.5 concentration. KDE is utilized to derive prediction intervals for PM2.5 concentration at confidence levels of 85%, 90%, and 95%, respectively, reflecting the uncertainty inherent in long-term trends of PM2.5. The performance of ConvFormer-KDE was compared with a list of advanced models. Experimental results showed that ConvFormer-KDE outperformed baseline models in long-term point- and interval-prediction tasks for PM2.5. The ConvFormer-KDE can provide a valuable early warning basis for future PM2.5 changes from the aspects of point and interval prediction.
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