Search Results (369)

The Air-front Smart City (ASC) concept is proposed to address the stagnation of industries in developed countries and stimulate economic growth in developing countries while maintaining a higher quality of life for people and contributing to decarbonization and overall United Nations SDGs in an existing study. However, no studies have been conducted to assess ASC policies. Therefore, this study integrates the integrated accessibility index into the quality of life (QOL) and quality of business (QOB) evaluation models to assess the startup ecosystem in Aichi, Singapore, and Munich within the ASC concept. The study uses survey data conducted in Aichi to estimate monetary values of QOL and QOB component indicators, calculates the integrated accessibility indices, and estimates QOL and QOB. Furthermore, the study sets scenarios to assess the impacts of living and business urban policies in Aichi. Additionally, the study using Aichi parameters compares the startup ecosystem in Singapore and Munich. The result shows that the key drivers of startup attraction are corporate tax rate, economic growth, and safety; enhancing these indicators directly increases startups’ QOB, business partners, and residents’ QOL. It was found that QOB in Singapore is comparatively higher, whereas QOL is higher in Aichi. Full article

Despite concern over air pollution, fine-scale spatial and demographic disparities in exposure remain largely unquantified in Australian cities due to sparse monitoring and coarse models. In Greater Melbourne, this gap limits neighbourhood-level assessment of PM_2.5 exposure and associated environmental inequalities. To address this gap, we integrated 6-month averaged PM_2.5 observations (October 2023 to March 2024) from 5 regulatory monitoring stations and 13 low-cost sensors (LCSs) to develop a land use regression (LUR) model estimating concentrations at a 100 m resolution. These estimates were used to calculate population-weighted PM_2.5 exposure (PWE) at the mesh block level across Melbourne. To examine factors associated with spatial heterogeneity in PWE, we applied a hybrid modeling framework combining Spatially Explicit Random Forest (Spatial-RF) and Geographically Weighted Regression (GWR), incorporating physical, built-environment, and socio-demographic variables from the Synthesized Multi-Dimensional Environmental Exposure Database (SEED). The Spatial-RF model initially exhibited an R² of 0.56. After multicollinearity diagnostics using the Variance Inflation Factor (VIF), three key explanatory variables were selected for GWR modeling: the Normalized Difference Vegetation Index (NDVI), the Index of Education and Occupation (IEO), and the proportion of culturally and linguistically diverse populations (CALDP). The developed GWR model achieved higher model performance (R² = 0.65) than Spatial-RF and global Ordinary Least Squares (OLS) regression (R² = 0.38), revealing strong spatial non-stationarity. Results show that PWE generally ranged from 5 to 7 µg/m³, exceeding the 2021 WHO air quality guideline, with hotspots in the urban core and along major transport corridors. Elevated exposure occurred in both socioeconomically disadvantaged areas and residents in urban centers with higher socio-economic status, reflecting complex, spatially contingent exposure inequalities. These findings support fine-scale, equity-oriented air quality management. Full article

The growth of human activity in cities is a key factor in the degradation of air quality. Numerous studies have demonstrated the link between air quality and the existence of dangerous and chronic diseases that are extremely costly for individuals and society. This study presents an analytical framework that compares fixed and mobile air-quality monitoring approaches in cities with limited resources, using Mohammedia city, Morocco, as an example. The framework centers on mobile monitoring units mounted on vehicles and equipped with affordable sensors, GPS technology, and wireless communication systems to track important pollutants, including fine particulate matter (PM_2.5 and PM₁₀) and harmful gaseous compounds (NO₂, SO₂, CO, O₃). The evaluation relies on scenario-based modeling, performance data from existing literature, and calculations of costs throughout the system’s lifetime. To enhance measurement reliability, the researchers developed a correction system that addresses measurement errors caused by temperature, humidity, vehicle speed, vibrations, traffic-related interference, operational interruptions, and communication limitations. The findings indicate that fixed monitoring stations deliver superior measurement precision, with estimated uncertainty ranging from ±1.2–2.5%, though their coverage area is restricted to 0.534 km² (representing 1.6% of Mohammedia). In comparison, the suggested mobile setup could potentially monitor 9.8 km², covering approximately 30% of the city, while decreasing infrastructure needs and setup time (2–4 h compared to 2–4 weeks). Over 10 years, the total cost is EUR 252,000 for mobile monitoring, compared with EUR 3.6 million for a network of 20 fixed stations. These results demonstrate that corrected mobile monitoring systems offer significant promise as an economical and sustainable approach for managing urban environmental conditions. Full article

Background/Objectives: Indoor air pollution is an increasingly recognized cause of lung cancer, yet evidence remains fragmented across exposure categories. This systematic review aimed to consolidate epidemiological findings on the relationship between household pollutants and lung cancer risk across diverse settings. Methods: A systematic search of PubMed, Web of Science, Scopus, and Cochrane was conducted to identify observational studies published between 2015 and 2025. Eligible articles evaluated indoor exposure in relation to primary lung cancer. Maximally adjusted effect estimates were extracted. Random effects models were used to calculate pooled odds ratios (ORs) and hazard ratios (HRs) when appropriate. Study quality was assessed using the Newcastle–Ottawa Scale. Results: Thirty-eight studies comprising 475,565 participants were included. Environmental tobacco smoke (ETS) was associated with lung cancer risk (pooled OR 1.97, 95% CI 1.63–2.37; pooled HR 1.44, 95% CI 1.19–1.74). Cooking oil fumes showed a pooled OR of 1.83 (95% CI 1.53–2.21). Solid fuel and biomass combustion were also associated with increased lung cancer risk, with pooled estimates indicating elevated odds and hazard ratios (pooled OR 2.26, 95% CI 1.36–3.77; pooled HR 1.66, 95% CI 1.37–2.02). Incense burning was evaluated in a single study (OR 3.05, 95% CI 1.06–8.84), with wide confidence intervals. Two studies explored gene–environment interactions, suggesting possible variability in susceptibility, although statistical robustness was limited. Conclusions: Across multiple exposure categories, indoor air pollution was consistently associated with lung cancer risk, although the effect magnitude and precision varied between studies. Given the observational nature of the evidence and methodological heterogeneity, further prospective research with standardized exposure assessment is needed to clarify the strength and consistency of these associations. Full article

The design of thermal strategies applied in buildings based on the use of renewable energies can play an important role in the development of a built environment that is better adapted to the climate. This paper is focused on the application of a renewable solar energy system coupled with a Heating, Ventilation and Air-Conditioned (HVAC) system to promote occupants’ thermal comfort (TC) and indoor air quality (IAQ) in buildings during heating season. In the building thermal design, a building thermal dynamic model is used to calculate the temperatures of the opaque and transparent building surfaces, the temperature of the water supply ducts, the TC level and the IAQ level, among other variables. The TC conditions of the occupants were evaluated using the Predicted Mean Vote index, commonly used in the literature in similar studies. IAQ was assessed by the usual carbon dioxide concentration in environments where most of the pollution is of human origin. The numerical study was carried out in a virtual residential building consisting of two floors and seven compartments. The building is occupied at night and at midday. Two cases were studied, considering, respectively, the non-use and use of the solar HVAC system. The solar HVAC system consists of solar water collectors, installed above the roof area, and thermo-convector heat exchangers, installed inside each occupied space. The results show that the application of this solar HVAC system in a Mediterranean-type climate is able to guarantee, during occupancy, acceptable TC levels in three compartments and near acceptable TC levels in one compartment. Regarding IAQ, acceptable level can be achieved throughout the day. Full article

Psoriasis is a common, chronic skin disorder that has negative impacts on patients’ quality of life, and is triggered by a combination of genetic and environmental factors. However, epidemiological evidence about the effect of air pollution on psoriasis risk is still limited and inconsistent. The generalized additive model (GAM) was applied to investigate the association between common air pollutants and daily psoriasis outpatient visits in Guangzhou, China from 2013 to 2019. The analysis focused on particulate matter with an aerodynamic diameter of less than 10 μm and 2.5 μm (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). To examine the effect modifications, stratified analyses were conducted by gender, age, and season. Population attributable fraction of psoriasis burden from ambient air pollution exposure was further calculated. A total of 145,034 psoriasis outpatient visits were included during the study period. Each 10 μg/m³ increment in PM_2.5, PM₁₀, SO₂, and NO₂ was significantly associated with an excess risk of psoriasis outpatient visits of 3.46% (95% CI: 2.53%, 4.39%), 2.51% (95% CI: 1.86%, 3.17%), 4.73% (95% CI: 2.67%, 6.82%), and 4.75% (95% CI: 3.78%, 5.73%) at lag05. Stratified analysis revealed notably stronger effects during the cold seasons. Based on the World Health Organization’s Ambient Air Quality Guidelines, PM_2.5, PM₁₀, NO₂, and SO₂ accounted for 9.08% (95% CI: 6.54%, 11.74%), 4.73% (95% CI: 3.45%, 6.06%), 8.93% (95% CI: 6.99%, 10.93%), and 0.18% (95% CI: 0.10%, 0.27%) of psoriasis outpatient visits, respectively. In conclusion, short-term air pollution exposure is an important risk factor for psoriasis outpatient visits, especially in cold seasons. PM_2.5 and NO₂ accounted for a relatively larger attributable burden among common air pollutants. Effective strategies are needed for air pollution control and prevention of psoriasis exacerbation. Full article

China has implemented a series of clean air policies, resulting in improved air quality since 2013. However, there remains a paucity of national prospective evidence regarding the relationship between fine particulate matter (PM_2.5) and kidney disease (KD) incidence in China, as well as the potential mediating effects of lipid profiles in this association. This study aimed to assess the association of decreased PM_2.5 concentration and KD incidence in China from 2013 to 2020. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), we included 15,368 participants who were free of KD in 2013 and followed up until 2020. For each participant, we calculated the 3-year and 2-year average PM_2.5 concentrations. The Cox proportional hazards model was employed to estimate the association between PM_2.5 exposure and KD incidence. Mediation analyses were conducted using eight lipid indices, and subgroup analyses were performed. The annual average PM_2.5 concentration for CHARLS participants reduced from 61.72 μg/m³ in 2013 to 32.75 μg/m³ in 2020. A reduction of 5 μg/m³ in 3-year and 2-year average PM_2.5 concentrations was associated with 14.3% (hazard ratio [HR]: 0.857, 95% confidence interval [CI]: 0.841, 0.873) and 14.4% (HR: 0.856, 95% CI: 0.840, 0.873) reductions in KD incidence in the fully adjusted models. The TyG-BMI and TyG-WHtR indices exhibited small mediating effects of 7.36% (95% CI: 2.35%, 12.38%) and 4.48% (95% CI: 0.51%, 8.45%) on the relationship of PM_2.5–KD, while other indicators did not demonstrate significant mediation. The findings of this study suggest that reductions in PM_2.5 concentration were associated with a decreased incidence of KD during the period from 2013 to 2020. The implementation of clean air policies since 2013 may have contributed to the decrease in chronic diseases like KD. Full article

Public health impacts of non-optimal temperatures and air pollution have received insufficient attention in Southeast Europe, one of the most air-polluted regions in Europe, simultaneously pressured by climate change. This study employed a multimodal approach to characterize the microclimate and air quality and conduct a health impact assessment in the three biggest cities in Bulgaria. Simulation of atmospheric thermo-hydrodynamics and assessment of urban microclimate relied on the Weather Research and Forecasting model. Concentrations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) were calculated with a land-use regression model. Ischemic heart disease (IHD) hospital admissions were linked to daily measurements at background air quality stations. The results showed declining trends in PM_2.5 but persistent levels of NO₂, especially in Sofia and Plovdiv. Distributed lag nonlinear models revealed that, in Sofia and Plovdiv, PM_2.5 was associated with IHD hospitalizations, with a fifth of cases in Sofia attributable to PM_2.5. For NO₂, an increased risk was observed only in Sofia. In Sofia, the risk of IHD was increased at cold temperatures, while both high and low temperatures were associated with IHD in Plovdiv and Varna. Short-term effects were observed in response to heat, while the effects of cold weather took up to several weeks to become apparent. These findings highlight the complexity of exposure–health interactions and emphasize the need for integrated policies addressing traffic emissions, urban design, and disease burden. Full article

Methoxy radicals (CH₃O•), formed as intermediates during methane oxidation, may play an underexplored but locally significant role in the atmospheric oxidation of dimethyl sulfide (DMS), a key sulfur-containing compound emitted primarily by marine phytoplankton. This study presents a comprehensive computational investigation of the reaction mechanisms and kinetics of DMS oxidation initiated by CH₃O•, using density functional theory B3LYP-D3(BJ)/6-311++G(3df,3pd), CCSD(T)/6-311++G(3df,3pd), and UCBS-QB3 methods. Our calculations show that DMS reacts with CH₃O• via hydrogen atom abstraction to form the methyl-thiomethylene radical (CH₃SCH₂•), with a rate constant of 3.05 × 10⁻¹⁶ cm³/molecule/s and a Gibbs free energy barrier of 14.2 kcal/mol, which is higher than the corresponding barrier for reaction with hydroxyl radicals (9.1 kcal/mol). Although less favorable kinetically, the presence of CH₃O• in localized, methane-rich environments may still allow it to contribute meaningfully to DMS oxidation under specific atmospheric conditions. While the short atmospheric lifetime of CH₃O• limits its global impact on large-scale atmospheric sulfur cycling, in marine layers where methane and DMS emissions overlap, CH₃O• may play a meaningful role in forming sulfur dioxide and downstream sulfate aerosols. These secondary organic aerosols lead to cloud condensation nuclei (CCN) formation, subsequent changes in cloud properties, and can thereby influence local radiative forcing. The study’s findings underscore the importance of incorporating CH₃O• driven oxidation pathways into atmospheric models to enhance our understanding of regional sulfur cycling and its impacts on local air quality, cloud properties and radiative forcing. These findings provide mechanistic insights that improve data interpretation for atmospheric models and extend predictions of localized variations in sulfur oxidation, aerosol formation, and radiative forcing in methane-rich environments. Full article

Automated precision drilling is essential for aircraft skin manufacturing, yet current robotic systems face dual challenges: chatter-induced inaccuracies in hole quality and limited access to confined spaces such as air inlets. To overcome these limitations, this paper develops a compact drilling robot for drilling large-curvature skins of aircraft air inlets. Targeting the precision drilling requirements for complex-curvature aircraft air inlets, we present the robot’s overall design scheme, detailing each module’s composition to ensure precision drilling. In-depth analysis of the robot’s large-curvature adaptability precisely calculates the wheel assembly dimensions. To ensure high-precision drilling bit entry into guide mechanisms, a flexible drilling spindle mechanism is designed, with calculated and verified elastic ranges. An integrated intelligent control system is developed, combining vision recognition, real-time pose adjustment, and automated drilling workflow planning. Finally, traversability and drilling capabilities are validated using a simplified air inlet model. Test results confirm successful traversal on R200 mm curvature skins and automated drilling of Carbon Fiber-Reinforced Polymer (CFRP)/7075 aluminum stacks with a diameter of Φ4–Φ6 mm, achieving dimensional errors of less than 0.05 mm and normal direction errors of less than 0.65°. Full article

An assessment of the effectiveness of measures undertaken to make urban road transport more sustainable requires appropriate tools to evaluate the impact of transport on air quality. For this purpose, emission inventories for the road network are prepared using suitable models. In cities without a calibrated travel demand model, the main challenge is obtaining data on traffic parameters. With this in mind, this study proposes a rapid model that enables the estimation of traffic parameters such as average speed, traffic volume, and the percentage share of individual vehicle categories. The proposed method is based on traffic measurements and the aggregation of different road classes into four cumulative categories. A comparison of results obtained from the simplified model and the travel demand model indicates satisfactory accuracy of the estimated parameters, confirming the usefulness of the proposed rapid model in emission inventory calculations. The performed calculations for PM_2.5 and NOx show that using traffic parameters derived from the rapid model, instead of those from a travel demand model, may result in an error of 15% in total emission for the traffic network. Full article

The effects of different activation functions (sigmoid and hyperbolic tangent), and node numbers in a hidden layer of artificial neural network (ANN) models on urban air quality forecasting were investigated in a coastal city of Korea. The ANN models of multilayer perceptron (MLP) with a back-propagation training algorithm for error calculation in cases of 13, 15, and 17 nodes in each hidden layer were performed using 15 input independent variables (PM, gas, and meteorological data of Gangneung city (Republic of Korea)), affected by PM and gas of an upwind Beijing city (China). Root mean square error (RMSE) and the coefficient of determination (R²; Pearson R) were evaluated to assess the two models’ forecasting abilities between the predicted and measured values. The values of R by ANN-sig (ANN-tanh) with 13, 15, and 17 hidden neuron numbers were 0.930 (0.950), 0.920 (0.947), 0.926(0.953) on PM₁₀, 0.953 (0.956), 0.927 (0.938), 0.949 (0.960) on PM_2.5, and 0.880 (0.959), 0.917 (0.886), and 0.882 (0.939) on NO₂. Regardless of node numbers and activation functions, the prediction abilities of the two models were excellent, showing the highest values of R in the ANN-tanh model with more neuron numbers in the hidden layer. Unlike previous studies’ insistence that smaller nodes (larger) in the hidden layer produce the overfit (underfit) result in the ANN model, the present study proves that more nodes in its hidden layer than the input layer can yield the best prediction than any other, as shown in their temporal distributions and scatter plots of predicted and measured data. Future-time air quality forecasting at Gangneung city can be calculated sequentially using its current time data and previous time data from Beijing city, using the suggested empirical formulas. Full article

Wintertime cold air pools (CAPs) are common across the Western United States and result in cold, dense air trapped in valley basins. The CAPs are characterized by a stable atmospheric boundary layer, leading to cold air and low wind speeds. While CAP formation occurs nightly, the CAP conditions can persist into daytime and often last for multiple days (i.e., persistent cold air pool or PCAP), resulting in poor air quality in populated areas. The presence and strength of CAPs can be calculated using data from radiosondes, surface weather stations at varying elevations, and indirectly through air pollution monitors. Because vertical profile data are often limited to twice daily radiosondes, and are spatially sparse, numerical models can be a useful substitute. This work uses the European Centre for Medium-Range Weather Forecasts (ECMWFs) Reanalysis v5 (ERA) atmospheric reanalysis to provide data to classify wintertime CAP events without radiosonde observations. An automated CAP classification method using ERA outputs is evaluated using afternoon radiosonde observations in six cities (Salt Lake City, Utah; Reno, Nevada; Boise, Idaho; Denver, Colorado; Las Vegas, Nevada; Medford, and Oregon). Using this CAP determination method, days with CAP events are analyzed in 13 locations, 6 with radiosonde observations and 7 without, including the Central valley of California. The CAP classification method is evaluated at these 13 locations across the Western US over the study period of 2000–2022. The results show that the ERA model performs similarly to the radiosonde observations when used to identify CAP events. Therefore, ERA can be used to provide a reasonable estimate of CAP conditions when radiosonde data are unavailable. Providing consistent CAP classifications across space and time are necessary for regional scale CAP studies, such as human health effects modeling over large spatial and temporal scales. Full article

Tree pollen is the most abundant in the Community of Madrid (CAM), and specifically, pollen types from Olea, Cupressaceae, and Platanus are the most allergenic, after Gramineae, in this Spanish region. Air pollutants are one of the most significant stress factors for wind-pollinated vegetation, especially in urban areas, and can cause alterations in the immune system and the consequent triggering of type I hypersensitivity reactions mediated by immunoglobulin E (IgE). This study analyses the allergic sensitization caused by the interrelation of O₃, NO₂, and PM₁₀ pollutants with the tree pollen types Olea, Cupressaceae, and Platanus in the period 2017–2021. To this end, general linear models were calculated using the Statgraphics Centurion 19 tool. The data collected came from the Air Quality Networks of the CAM and Madrid City Council, the CAM Palynological Network, and the Allergy Services of the reference hospitals in the five study areas. This research confirms a statistically significant correlation between allergic sensitivity to pollen types and their concentrations in the air, and those of atmospheric pollutants, in the different areas and years studied. These pollen and pollutant concentrations in the atmosphere of the CAM jointly influence the prevalence of allergic sensitisation, as is evident in all the models calculated. Full article

Temperature excursions during refrigerated transport strongly affect the quality and shelf life of perishable food, yet reproducing realistic, time-varying cold-chain temperature histories in the laboratory remains challenging. In this study, we present a compact, portable climate chamber driven by Peltier modules and an identification-guided control architecture designed to reproduce real refrigerated-truck temperature histories with high fidelity. Control is implemented as a cascaded regulator: an outer two-degree-of-freedom PID for air-temperature tracking and faster inner PID loops for module-face regulation, enhanced with derivative filtering, anti-windup back-calculation, a Smith predictor, and hysteresis-based bumpless switching to manage dead time and polarity reversals. The system integrates distributed temperature and humidity sensors to provide real-time feedback for precise thermal control, enabling accurate reproduction of cold-chain conditions. Validation comprised two independent 36-day reproductions of field traces and a focused 24-h comparison against traditional control baselines. Over the long trials, the chamber achieved very low long-run errors ($\mathrm{MAE}\cong 0.19 °\mathrm{C}$, $\mathrm{MedAE}\cong 0.10 °\mathrm{C}$, $\mathrm{RMSE}\cong 0.33 °\mathrm{C}$, $R^2=0.9985$). The 24-h test demonstrated that our optimized controller tracked the reference, improving both transient and steady-state behaviour. The system tolerated realistic humidity transients without loss of closed-loop performance. This portable platform functions as a reproducible physical twin for cold-chain experiments and a reliable data source for training predictive shelf-life and digital-twin models to reduce food waste. Full article