Search Results (277)

International air quality indexes (AQIs) are derived from air pollution and are essential global tools for mitigating diseases such as asthma, as they are used to reduce exposure to triggers. The aim of this article is to systematically review the global literature on the use of AQIs in asthma-related studies. To evaluate the importance of the variables considered, a citation frequency index (Q) was used. The results suggest that the most frequently reported air pollutants related to asthma are PM (Q3) > NO₂ (Q3) > O₃ (Q3) > CO (Q3) > NO (Q3) > SO₂ (Q3). In addition, climate variables play a relevant role in asthma research. Temperature (Q4) emerged as the most relevant climate variable, followed by atmospheric pressure (Q3) > wind direction (Q3) > solar radiation (Q3) > precipitation (Q3) > wind speed (Q3). AQIs, specifically the U.S.EPA Air Quality Index and the Air Quality Health Index, are directly associated with air pollution and the prevalence, severity and exacerbation of asthma. The findings also suggest that climate change presents additional challenges in relation to asthma by influencing the environmental conditions that affect the disease. Finally, this study provides a comprehensive view of the relationships among air quality, air pollutants and asthma and highlights the need for further research in this field to develop public health policies and environmental regulations. Full article

Urban air pollution is a critical global environmental issue, necessitating an analysis of the spatiotemporal characteristics of air quality and its driving factors for sustainable urban development. However, the traditional urban air quality assessment system ignores the impact of internal urban spatial structures. Therefore, this paper proposes an assessment system that integrates natural, socio-economic, and urban layout factors by utilizing the air quality index (AQI) and 14 types of multi-source geographic data in the main urban area of Beijing from 2016 to 2020 and constructs geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR) models for spatiotemporal analysis. Our findings revealed an annual improvement in air quality, with a U-shaped seasonal pattern and significant spatial clustering (Global Moran’s I = 0.922). The MGWR model provided a superior fit over the GWR, capturing spatial variability more effectively. Variables such as NDVI, economic output (GDP), and humidity space adjustment capability (HSAC) showed significant positive spatial impacts on air quality, while population density (POP), temperature (TEMP), and road density (RD) exhibited negative effects. These results explain the changes in air quality in the main urban area of Beijing from a spatiotemporal perspective and provide planning input for urban air quality regulations. Full article

Based on data from the China Migrant Dynamic Survey project and urban statistics, this article examines the impact of air pollution on the residence intentions of rural migrants. The research findings indicate that: (1) Air pollution reduces the residence intentions of rural migrants. On average, for every one-unit increase in AQI, the residence intention of rural migrants will reduce by 1.5l%. (2) Mechanism analysis shows that social networks and social integration have a negative moderating effect on the relationship between air pollution and the residence intention of rural migrants. (3) Heterogeneity analysis shows that in cities north of the Qinling Mountains-Huaihe River, cities with low precipitation, and cities with weak environmental regulations, the negative impact of air pollution on residence intention of rural migrants is more significant. Compared with high human capital levels, inter-provincial flow, and the new generation of rural migrants, the residence intention of low human capital levels, intra-provincial flow, and the old generation of rural migrants makes them more vulnerable to the negative impact of air pollution. This article reveals the inherent relationship between air pollution and the residence intention of rural migrants, which has certain practical enlightenment for cities to accelerate the process of citizenization of rural migrants through air pollution control and also provides important empirical evidence for cities to sustainably address air pollution. Full article

Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality’s temporal and spatial distribution characteristics. In this study, Xi’an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi’an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model’s hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model’s accuracy and its promising application prospects. Full article

Rapid urbanization has resulted in increased environmental challenges, compounding worries about deteriorating air quality and rising temperatures. As cities become hubs of human activity, understanding the complex interplay of numerous environmental elements is critical for developing effective mitigation solutions. Recognizing this urgency, a framework to highlight the hotspots with critical environmental issues emerges as a comprehensive approach that incorporates key criteria such as the surface urban heat island intensity (SUHII), heat index (HI) and air quality index (AQI) to assess and address the complex web of environmental stressors that grip urban landscapes. Employing the multicriteria decision analysis approach, the proposed framework, named the environmental risk hotspot mapping framework (ERHMF), innovatively applies the analytic hierarchy process at a sub-criteria level, considering long-term heat island trends with recent fluctuations in the HI and AQI. Climate change impact has been symbolized through rising temperatures, as reflected by surface urban heat island intensity trends over two decades. The robustness and correctness of the weights have been assessed by computing the consistency ratio, which came out as 0.046, 0.065 and 0.044 for the sub-criteria of the SUHII, AQI and HI, respectively. Furthermore, the framework delves into the nexus between environmental stressors and vegetation cover, elucidating the role of green spaces in mitigating urban environmental risks. Augmented by spatial and demographic data, the ERHMF adeptly discerns high-risk areas where environmental stress converges with urban development, vulnerable population concentrations and critical vegetation status, thereby facilitating targeted risk management interventions. The framework’s effectiveness has been demonstrated in a regional case study in Italy, underscoring its ability to pinpoint risk hotspots and inform specific policy interventions. The quantitative study undertaken at the sub-administrative level revealed that approximately 6,000,000 m² of land in Bologna are classified as being under high to extremely high environmental stress, with over 4,000,000 m² lying only within the extremely high stress group (90–100). Similarly, 1,000,000 m² of land in Piacenza and Modena have high levels of environmental stress (80–90). In conclusion, the ERHMF presents a holistic methodology for delineating high-risk urban hotspots, providing essential insights for policymakers, urban planners and stakeholders, with the potential to enhance overall urban resilience and foster sustainable development efforts. Full article

This paper discusses air quality index (AQI) representation using a fuzzy logic framework to cover the blurry areas of AQI where indices are in between ranges of values. After studying several standards for air quality prediction (AQP), this research suggested the use of fuzzy logic as an extended method to cover some limitations found in several standards, in which the fuzzy logic represents a more dynamic way to support cross-country comparisons as well. This research expanded upon the United States Environmental Protection Agency (USEPA) standards to address their acknowledged limitations by constructing a fuzzy air quality levels prediction (FAQLP) model, which categorizes air quality into corresponding ranges (actual levels) and classifies new fuzzy levels (predicted levels), using a fuzzy logic model (to enforce more realistic predictions). This model can solve the issue of values at or near boundaries when there is uncertainty about air quality levels. The study aims to incorporate a comparative study of two urban settings providing dynamic machine-learning modeling approaches for advanced air pollution control. The DNN–Markov model is presented in this paper as the selected hybrid model for AQI prediction, and the adaptive neuro-fuzzy inference system (ANFIS) was used to represent AQI. This work presents a novel air quality index framework that consists of a DNN–Markov model for accurate hourly predictions and air quality level representations using ANFIS. Full article

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM_2.5 and PM₁₀ were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM₁₀ (50 μg/m³) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area. Full article

Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R²) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction. Full article

Understanding air microbial content, especially in highly polluted urban areas, is crucial for assessing its effect on human health and ecosystems. In this context, the impact of gaseous pollutants on the aerobiome remains inconclusive due to a lack of studies separating this factor from other contaminants or environmental factors. In this study, we aimed to experimentally assess the influence of contrasting concentrations of atmospheric gaseous pollutants as isolated variables on the composition of the aerobiome. Our study sites were contrasting Air Quality Index (AQI) sites of the Metropolitan Region of Chile, where nitric oxide (NO) was significantly lower at the low-AQI site than at the high-AQI site, while ozone (O₃) was significantly higher. Cultivable aerobiome communities from the low-AQI site were exposed to their own pollutants or those from the high-AQI site and characterized using high-throughput sequencing (HTS), which allowed comparisons between the entire cultivable communities. The results showed increased alpha diversity in bacterial and fungal communities exposed to the high-AQI site compared to the low-AQI site. Beta diversity and compositional hierarchical clustering analyses revealed a clear separation based on NO and O₃ concentrations. At the phylum level, four bacterial and three fungal phyla were identified, revealing an over-representation of Actinobacteriota and Basidiomycota in the samples transferred to the high-AQI site, while Proteobacteria were more abundant in the community maintained at the low-AQI site. At the functional level, bacterial imputed functions were over-represented only in samples maintained at the low-AQI site, while fungal functions were affected in both conditions. Overall, our results highlight the impact of NO and/or O₃ on both taxonomic and functional compositions of the cultivable aerobiome. This study provides, for the first time, insights into the influence of contrasting pollutant gases on entire bacterial and fungal cultivable communities through a controlled environmental intervention. Full article

The escalating concern regarding increasing air pollution and its impact on the health risks associated with PM_2.5 in developing countries necessitates attention. Thus, this study utilizes the WRF-CMAQ model to simulate the effects of meteorological conditions on PM_2.5 levels in Changchun, a typical city in China, during January 2017 and January 2020. Additionally, it introduces a novel health risk-based air quality index (NHAQI) to assess the influence of meteorological parameters and associated health risks. The findings indicate that in January 2020, the 2-m temperature (T2), 10-m wind speed (WS10), and planetary boundary layer height (PBLH) were lower compared to those in 2017, while air pressure exhibited a slight increase. These meteorological parameters, characterized by reduced wind speed, heightened air pressure, and lower boundary layer height—factors unfavorable for pollutant dispersion—collectively contribute to the accumulation of PM_2.5 in the atmosphere. Moreover, the NHAQI proves to be more effective in evaluating health risks compared to the air quality index (AQI). The annual average decrease in NHAQI across six municipal districts from 2017 to 2020 amounts to 18.05%. Notably, the highest health risks are observed during the winter among the four seasons, particularly in densely populated areas. The pollutants contributing the most to the total excess risk (ERtotal) are PM_2.5 (45.46%), PM10 (33.30%), and O3 (13.57%) in 2017, and PM_2.5 (67.41%), PM10 (22.32%), and O3 (8.41%) in 2020. These results underscore the ongoing necessity for PM_2.5 emission control measures while emphasizing the importance of considering meteorological parameters in the development of PM_2.5 reduction strategies. Full article

Ongoing climate change is projected to intensify drought stress globally. Understanding the response mechanisms of Phyllostachys edulis (Carrière) J. Houz. (moso bamboo) to long-term drought is crucial, given its significance as a carbon sequestration resource. In this study, precipitation exclusion was implemented to simulate drought stress and we investigated the effects of long-term drought on the photosynthetic parameters, stomatal conductance, and water use efficiency of moso bamboo. The results showed that throughout all growth seasons, the maximum net photosynthetic rates (P_max) of bamboo at all ages under long-term drought conditions (after 8 years of precipitation exclusion treatment) were significantly lower than those of the control (p < 0.05). It can be concluded that long-term drought reduced the maximum photosynthetic capacity of the bamboo at all ages. Under long-term drought conditions, there were many seasons where the light saturation point (LSP) of first-degree (1–2 years old) bamboo and third-degree (5–6 years old) bamboo under drought was significantly lower than those of the control, while the LSP value of second-degree (3–4 years old) bamboo under drought was significantly higher than that of the control. This suggests that long-term drought reduced the ability of first-degree and third-degree bamboo to utilize strong light, while improving the ability of second-degree bamboo to utilize strong light in summer, autumn, and winter. Under long-term drought conditions, the light compensation point (LCP) and the apparent quantum efficiency (AQY) of the bamboo decreased. It can be concluded that long-term drought reduced the ability of first-degree bamboo to utilize weak light in all seasons, as well as the ability of second-degree bamboo to utilize weak light in spring and autumn; meanwhile, it improved the ability of second-degree bamboo to utilize weak light in summer and winter, and the ability of third-degree bamboo to utilize weak light in spring, summer, and autumn. In the high light range (PARi > 1000 µmol · m⁻² · s⁻¹), there were significant differences in stomatal conductance (g_s) among different the different treatments of bamboo, which were influenced by both the growing season and the forest age. Compared to the control, under drought conditions, the stomatal conductance of third-degree bamboo increased in spring and that of the second-degree bamboo increased in autumn. The correlation analysis showed that the relationship between the stomatal conductance and vapor pressure deficit (VPDL) of bamboo under long-term drought conditions showed a significant polynomial relationship in both high and low light ranges. The correlation between the instantaneous water use efficiency (iWUE) and VPDL for the drought and control treatments of bamboo also showed a significant polynomial relationship in high light ranges. It was found that long-term drought changed the photosynthetic parameters of the bamboo, reflecting its ability to tolerate and adapt to drought in different seasons. Age-related differences in photosynthetic parameters should be fully considered in forest age structure adjustments and forest thinning procedures to strengthen the light intensity and maintain the opening of the stoma. These results provide a theoretical basis for the efficient and sustainable cultivation of bamboo under global climate change. Full article

Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F₁ score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O₃ predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting. Full article

In this paper, the design and research of a sensor-based personal air-quality monitoring device are presented, which is retrofitted into different personal protective face masks. Due to its small size and low power consumption, the device can be integrated into and applied in practical urban usage. We present our research and the development of the sensor node based on a BME680-type environmental sensor cluster with a wireless IoT (Internet of Things)-capable central unit and overall low power consumption. The integration of the sensor node was investigated with traditional medical masks and a professional FFP2-type mask. The filtering efficiency after embedding was validated with a head model and a particle counter. We found that the professional mask withstood the embedding without losing the protective filtering aspect. We compared the inner and outer sensor data and investigated the temperature, pressure, humidity, and AQI (Air Quality Index) relations with possible sensor data-fusion options. The novelty is increased with the dual-sensor layout (inward and outward). It was found that efficient respiration monitoring is achievable with the device. With the analysis of the recorded data, characteristic signals were identified in an urban environment, enabling urban altimetry and urban zone detection. The results promote smart city concepts and help in endeavors related to SDGs (Sustainable Development Goals) 3 and 11. Full article

The SARS-CoV-2 global pandemic prompted governments, institutions, and researchers to investigate its impact, developing strategies based on general indicators to make the most precise predictions possible. Approaches based on epidemiological models were used but the outcomes demonstrated forecasting with uncertainty due to insufficient or missing data. Besides the lack of data, machine-learning models including random forest, support vector regression, LSTM, Auto-encoders, and traditional time-series models such as Prophet and ARIMA were employed in the task, achieving remarkable results with limited effectiveness. Some of these methodologies have precision constraints in dealing with multi-variable inputs, which are important for problems like pandemics that require short and long-term forecasting. Given the under-supply in this scenario, we propose a novel approach for time-series prediction based on stacking auto-encoder structures using three variations of the same model for the training step and weight adjustment to evaluate its forecasting performance. We conducted comparison experiments with previously published data on COVID-19 cases, deaths, temperature, humidity, and air quality index (AQI) in São Paulo City, Brazil. Additionally, we used the percentage of COVID-19 cases from the top ten affected countries worldwide until May 4th, 2020. The results show $80.7$% and $10.3$% decrease in RMSE to entire and test data over the distribution of 50 trial-trained models, respectively, compared to the first experiment comparison. Also, model type#3 achieved 4th better overall ranking performance, overcoming the NBEATS, Prophet, and Glounts time-series models in the second experiment comparison. This model shows promising forecast capacity and versatility across different input dataset lengths, making it a prominent forecasting model for time-series tasks. Full article

In steel recycling, the optimization of Electric Arc Furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie–Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie–Mayr model. Based on the Cassie–Mayr arc parameters, a novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology. Full article