Search Results (79)

Air quality (AQ) modeling is at the forefront of estimating pollution levels in areas where the spatial representativity is low. Large metropolitan areas in Asia such as Beijing face significant pollution issues due to rapid industrialization and urbanization. AQ nowcasting, especially in dense urban centers like Beijing, is crucial for public health and safety. One of the most popular and accurate modeling methodologies relies on black-box models that fail to explain the phenomena in an interpretable way. This study investigates the performance and interpretability of Explainable AI (XAI) applied with the eXtreme Gradient Boosting (XGBoost) algorithm employing the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model-Agnostic Explanations (LIME) for PM_2.5 nowcasting. Using a SHAP-based technique for dimensionality reduction, we identified the features responsible for 95% of the target variance, allowing us to perform an effective feature selection with minimal impact on accuracy. In addition, the findings show that SHAP and LIME supported orthogonal insights: SHAP provided a view of the model performance at a high level, identifying interaction effects that are often overlooked using gain-based metrics such as feature importance; while LIME presented an enhanced overlook by justifying its local explanation, providing low-bias estimates of the environmental data values that affect predictions. Our evaluation set included 12 monitoring stations using temporal split methods with or without lagged-feature engineering approaches. Moreover, the evaluation showed that models retained a substantial degree of predictive power (R² > 0.93) even in a reduced complexity size. The findings provide evidence for deploying interpretable and performant AQ modeling tools where policy interventions cannot solely depend on predictive analytics tools. Overall, the findings demonstrate the large potential of directly incorporating explainability methods during model development for equal and more transparent modeling processes. Full article

Air-quality standards (AQS) are key regulatory tools to protect public health by setting pollutant thresholds. However, most are based on sea-level data. High-altitude (HA) environments differ in atmospheric conditions, influencing pollutant behavior and human vulnerability. These differences have prompted proposals for altitude-specific AQS adjustments. This systematic review identifies models and criteria supporting such adaptations and examines regulatory air-quality frameworks in countries with substantial populations living at very high altitudes (VHA). This review follows PRISMA-P guidelines, focusing on studies examining AQS adjustment approaches based on altitude. The Population/Concept/Context (PCC) framework was used to define search terms: population (AQS), concept (air pollutants), and context (altitude), with equivalents. The literature was retrieved from PubMed, Scopus, Web of Science, and Gale OneFile: Environmental Studies and Policy. A total of 2974 articles were identified, with 2093 remaining after duplicate removal. Following title and abstract screening, 2081 papers were excluded, leaving 12 for full-text evaluation. Ultimately, six studies met the eligibility criteria. Three studies focused on adjustment models based on atmospheric conditions, such as temperature and pressure changes, while the other three examined human physiological responses, particularly the increased inhaled air volume. China, Peru, and Bolivia have the largest populations living above 3500 m a.s.l., yet none of these countries have specific air-quality regulations tailored to HA conditions. The review underscores the necessity for tailored AQS in HA environments, highlighting specific criteria related to both atmospheric conditions and human physiological responses. Full article

Artificial intelligence (AI) technology not only promotes rapid economic development but also plays an irreplaceable role in improving environmental quality. Based on the quasi-natural experiment of the National Artificial Intelligence Innovation Comprehensive Experimental Zone, this paper empirically studies the effect and mechanism of AI on urban air quality (AQ) using the multi-time difference-in-difference model. The research results showed that AI improved the AQ of cities. The mechanism analysis results indicated that there was a positive mediating effect of government environmental attention on the relationship between AI and AQ improvement. Public environmental attention can further enhance the role of AI in improving urban AQ. Further analysis revealed that the improvement effect of AI on urban AQ was mainly reflected in eastern cities and non-resource-based cities. The research conclusion of this study provides reliable empirical evidence for leveraging AI to empower urban green development and assist in air pollution prevention practices. Full article

Air quality (AQ) is one of the most important urban environment indicators for the quality of life. The paper proposes a software solution for predicting and forecasting the air quality index (AQI) in urban areas. The study integrates pollutant factors (CO, NO₂, SO₂, PM_2.5), meteorological parameters (temperature, humidity, wind speed), and traffic data to determine air quality. For this purpose, 19 predictive models were developed and compared: 12 machine learning algorithms, 7 deep learning, and 1 forecasting model based on structural component analysis. The Random Forest Regression model, customized within the study, achieved the best results, with an R² score of 99.59%, an MAE of 0.22%, an MAPE of 0.68%, and an OP (Overall Precision) score of 95.61%. It was subsequently validated on unseen data and recorded a mean deviation of 0.58%. For short-term AQI forecasting (5 days), the AQIF model achieved an R² of 71.62%, an MAE of 0.4%, and an MAPE of 0.9%. The proposed solution was integrated into a web application with IoT infrastructure and real-time alert mechanisms. Future directions include expanding the dataset and optimizing hyperparameters for the deep learning models to increase accuracy, as well as integrating PM₁₀ and O₃ factors, along with the degree of industrialization and demographic level. Full article

Air Quality (AQ) and the management of low-emission zones are critical issues in densely populated urban areas. In such environments, human activity significantly impacts AQ, prompting increased efforts to monitor it using a range of devices. Traditional Air Quality monitoring relies on regulated stations, which are often scarce due to high costs, leaving many areas unmonitored. Low-cost sensors offer a promising solution by enabling the higher-spatial-resolution monitoring of pollution levels. In this article, we present the results of a case study conducted in an urban setting where AQ is affected by human activity, particularly during Las Fallas, Valencia’s most renowned festival, which has been declared an Intangible Cultural Heritage of Humanity by UNESCO. The festival features widespread bonfires, firecrackers and large crowds, all of which contribute to worsening air pollution. In this context, we evaluate the performance of the off-the-shelf, low-cost ZPHS01B multisensor module in a real deployment. This module is capable of monitoring Temperature (T), Relative Humidity (RH), Particulate Matter (PM), CO, ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, ${\mathrm{O}}_3$, ${\mathrm{CH}}_2$O and Volatile Organic Compounds. We analyze the features and properties of these sensors. In our deployments, the ZPHS01B module is connected to an ESP32 microcontroller and assembled into an AQ Internet of Things (IoT) node. We present AQ monitoring results from the festival and compare the measurements with those from regulated AQ monitoring stations, used as a reference. Additionally, we evaluate the power consumption of this AQ IoT node, providing its electrical operating characteristics and considering the use of duty cycles to reduce consumption while maintaining sensor stability. We conclude that this module offers promising capabilities for identifying pollution risk zones and opens the door to new research opportunities, particularly in efficient sensor calibration and AQ parameter prediction. Full article

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% increase in agreement between our produced masks and high-certainty hand-labeled pixels, relative to evaluated operational products, the demonstrated approach successfully differentiates active fire pixels and smoke plumes from background imagery. This enables the generation of a per-instrument smoke and active fire mask product, as well as smoke and fire masks created from the fusion of selected data from independent instruments. This ML approach has the potential to enhance operational active wildfire monitoring systems and improve decision-making in air quality management through fast smoke plume identification and tracking and could improve climate impact studies through fusion data from independent instruments. Full article

Densely populated urban areas often experience poor air quality due to high levels of anthropogenic emissions. The population is frequently exposed to harmful gaseous and particulate pollutants, which are directly linked to various health issues, including respiratory diseases. Accurately assessing and predicting pollutant concentrations within urban areas is therefore crucial. This study developed a computational fluid dynamic (CFD) model designed to capture turbulence effects that influence pollutant dispersion in urban environments. The focus was on key pollutants commonly associated with vehicular emissions, such as carbon monoxide (CO), nitrogen oxides (NO_x), volatile organic compounds (VOCs), and particulate matter (PM). The model was applied to the city of Augsburg, Germany, to simulate pollutant behavior at a microscale level. The primary objectives were twofold: first, to accurately predict local pollutant concentrations and validate these predictions against measurement data; second, to evaluate the representativeness of air quality monitoring stations in reflecting the broader pollutant distribution in their vicinity. The approach presented here has demonstrated that when focusing on an area within a specific radius of an air quality station, the representativeness ranges between 10% and 16%. On the other hand, when assessing the representativeness across the street of deployment, the spatial coverage of the sensor ranges between 23% and 80%. This analysis highlights that air quality stations primarily capture pollution levels from high-activity areas directly across their deployment site, rather than reflecting conditions in nearby lower-activity zones. This approach ensures a more comprehensive understanding of urban air pollution dynamics and assesses the reliability of air quality (AQ) monitoring stations. Full article

Traffic-related air pollution significantly affects air quality. Many cities have introduced low emission zones (LEZs) to restrict urban transport. Nitrogen dioxide (NO₂) is a dangerous pollutant associated with adverse health effects, such as respiratory diseases, cancer, and death. This research aimed to evaluate the impact of implementing an LEZ during an informative period in which no fines were imposed on vehicles. The methodology consisted of several phases. Firstly, the legal levels to guarantee compliance with air quality standards of the Directive 2008/50/EC were studied. Secondly, this study analyzed the temperature and wind speed patterns of the city under investigation. Finally, an in-depth statistical study evaluated the impact of the LEZ at each air quality monitoring station throughout the municipality. The case study focused on Seville, Spain, using data from 2022, 2023, and the first quarter of 2024, the latter corresponding to the reporting period without fines. The results reveal a wide dispersion and periodicity in NO₂ concentrations at the monitoring stations. Seville complied with NO₂ air quality regulations before the implementation of the LEZ, with similar seasonal patterns observed. A low overall impact was observed in the first three months after implementation. This methodology can be used universally. Full article

This study examines long-term trends in fine particulate matter (PM_2.5) composition and oxidative potential in Los Angeles based on data from the University of Southern California’s Particle Instrumentation Unit, with chemical composition retrieved from the EPA’s Air Quality System (AQS). While regulatory interventions have reduced PM_2.5 mass concentration and primary combustion-related components, our findings reveal a more complex toxicity pattern. From 2001 to 2008, the PM_2.5 oxidative potential, measured via the dithiothreitol (DTT) assay, declined from ~0.84 to ~0.16 nmol/min/m³ under stringent tailpipe controls. However, after this initial decline, PM_2.5 DTT stabilized and gradually increased from ~0.35 in 2012 to ~0.97 nmol/min/m³ by 2024, reflecting the growing influence of non-tailpipe emissions such as brake/tire wear. Metals, such as iron (Fe, ~150 ng/m³) and zinc (Zn, ~10 ng/m³), remained relatively stable as organic and elemental carbon (OC and EC) declined, resulting in non-tailpipe contributions dominating PM_2.5 toxicity. Although PM_2.5 mass concentrations were effectively reduced, the growing contribution of non-tailpipe emissions (e.g., brake/tire wear and secondary organic aerosols) underscores the limitations of mass-based standards and tailpipe-focused strategies. Our findings emphasize the need to broaden regulatory strategies, targeting emerging sources that shape PM_2.5 composition and toxicity and ensuring more improvements in public health outcomes. Full article

Air pollution is the second leading cause of death from non-communicable diseases. In Guanajuato, Mexico, the brick industry is the main economic source of polluting emissions, with the greatest health impacts. This sector has initiated government regulatory changes, but there is currently no monitoring of its impact on health. As a first pilot phase, this study’s objective was to measure the main air pollutants in a rural community in Guanajuato, Mexico, using a low-cost ATMOTUBE^® monitor and to describe the area and population group at the greatest risk of exposure. An analytical and longitudinal design from September 2023 to February 2024, with the ATMOTUBE^® measurement parameters VOC, PM₁, PM_2.5, PM₁₀, temperature, humidity, and pressure, was used. During the six months of measurement, the results were as follows: a VOC of 4.15 ± 11.79 ppm, an Air Quality Score (AQS) of 65.17 ± 30.11, and a PM₁ value of 4.90 ± 18.43 μg/m³. January–February 2024 was the period with the highest concentration of pollutants, with a maximum PM_2.5 concentration of 664 ± 12.5 μg/m³, a maximum PM₁₀ concentration of 650 ± 14.8 μg/m³, and a low humidity value (34.1 ± 5.2%). These values were found near two schools. The first inventory of the main air pollutants in this rural community is presented, with children and women being the population at greatest risk. With these data from this pilot phase, it is recommended to start implementing surveillance measures alongside health and nutrition indicators, mainly for the vulnerable population of this rural community. Full article

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death in Europe. Urban populations are particularly exposed to high concentrations of air pollutants, such as particulate matter smaller than 10 µm (PM₁₀). Understanding the spatiotemporal variations of PM₁₀ is essential for developing effective control strategies. This study aimed to enhance PM₁₀ prediction models by integrating landscape metrics as ecological indicators into our previous models, assessing their significance in monthly average PM₁₀ concentrations, and analyzing their correlations with PM₁₀ air pollution across European urban landscapes during heating (cold) and non-heating (warm) seasons. In our previous research, we only calculated the proportion of land uses (PLANDs), but according to our current research hypothesis, landscape metrics have a significant impact on PM₁₀ air quality. Therefore, we expanded our independent variables by incorporating landscape metrics that capture compositional heterogeneity, including the Shannon diversity index (SHDI), as well as metrics that reflect configurational heterogeneity in urban landscapes, such as the Mean Patch Area (MPA) and Shape Index (SHI). Considering data from 1216 European air quality (AQ) stations, we applied the Random Forest model using cross-validation to discover patterns and complex relationships. Climatological factors, such as monthly average temperature, wind speed, precipitation, and mean sea level air pressure, emerged as key predictors, particularly during the heating season when the impact of temperature on PM₁₀ prediction increased from 5.80% to 22.46% at 3 km. Landscape metrics, including the SHDI, MPA, and SHI, were significantly related to the monthly average PM₁₀ concentration. The SHDI was negatively correlated with PM₁₀ levels, suggesting that heterogeneous landscapes could help mitigate pollution. Our enhanced model achieved an R² of 0.58 in the 1000 m buffer zone and 0.66 in the 3000 m buffer zone, underscoring the utility of these variables in improving PM₁₀ predictions. Our findings suggest that increased urban landscape complexity, smaller patch sizes, and more fragmented land uses associated with PM₁₀ sources such as built-up areas, along with larger and more evenly distributed green spaces, can contribute to the control and reduction of PM₁₀ pollution. Full article

The air quality (AQ) of a given location depends mostly on two factors: emissions and meteorological conditions. For most places on Earth, the meteorology of an area changes seasonally. For central Europe, winters are associated with poor dispersion conditions, which, in combination with high emissions from local heating systems, lead to significantly higher concentrations than during summer. In this study, the seasonality of AQ is analysed using hourly measurements from 44 monitoring stations in Slovakia for the years 2007–2023 for NO₂, PM₁₀ and PM_2.5. Two factors are used to evaluate the seasonality—the difference and ratio of the winter and summer mean concentrations. It was found that the seasonal difference has been gradually decreasing for all pollutants since 2017. In the case of PM_2.5, the seasonal ratio drops from a value of around 2.5 in 2018 to approximately 1.7 in 2023. While in the past, the seasonal ratio was the highest for PM_2.5, in the last three years it is the highest for NO₂ with values larger than 2. Our results imply that summer sources of PM emissions start to play a more important role for the AQ than in the past. The observed seasonality was compared with two full-year chemical-transport model simulations. Full article

This study aims to scientifically justify the identification of suitable urban properties for urban green infrastructure (UGI) interventions to optimize its natural regulating functions for long-term pollution mitigation and secondary dust reduction. This study adheres to the perception that planning urban transformations to improve ambient air quality (AQ) requires a thorough understanding of urban structural heterogeneity and its interrelationship with the local microclimate. We apply an approach in which UGI and its potential multifunctionality are explored as a structural–functional element of urban local climatic zones. The same (100 × 100 m) spatial framework is used to develop place-based adapted solutions for intervention in UGI. A complex geospatial analysis of Burgas City, the second largest city (by area) in Bulgaria, was conducted by integrating 12 indicators to reveal the spatial disbalance of AQ regulation’ demand and UGI’s potential to supply ecosystem services. A total of 174 municipally owned properties have been identified, of which 79 are of priority importance, including for transport landscaping, inner-quarter spaces, and social infrastructure. Indicators of population density and location of social facilities were applied with the highest weight in the process of prioritizing sites. The study relies on public data and information from the integrated city platform of Burgas, in cooperation with the city’s government. The results have been discussed with stakeholders and implemented by the Municipality of Burgas in immediate greening measures in support of an ongoing program for Burgas Municipality AQ improvement. Full article

Assessing air quality in urban areas is vital for protecting public health, and low-cost sensor networks help quantify the population’s exposure to harmful pollutants effectively. This paper introduces an innovative method to calibrate air-quality sensor networks by combining CFD modeling with dependable AQ measurements. The developed CFD model is used to simulate traffic-related PM₁₀ dispersion in a 1.6 × 2 km² urban area. Hourly simulations are conducted, and the resulting concentrations are cross-validated against high-quality measurements. By offering detailed 3D information at a micro-scale, the CFD model enables the creation of concentration maps at sensor locations. Through regression analysis, relationships between low-cost sensor (LCS) readings and modeled outcomes are established and used for network calibration. The study demonstrates the methodology’s capability to provide aid to low-cost devices during a representative 24 h period. The precision of a CFD model can also guide optimal sensor placement based on prevailing meteorological and emission scenarios and refine existing networks for more accurate urban air quality representation. The usage of cost-effective air quality networks, high-quality monitoring stations, and high-resolution air quality modeling combines the strengths of both top-down and bottom-up approaches for air quality assessment. Therefore, the work demonstrated plays a significant role in providing reliable pollutant monitoring and supporting the assessment of environmental policies, aiming to address health issues related to urban air pollution. Full article

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 (NO_x), 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. Full article