Abstract - Air pollution has become a serious environmental and public health issue in urban areas, driven by factors such as rapid urbanization, increased vehicular traffic, and industrial activities. Predicting air pollution in a specific city is challenging due to the dynamic and interrelated nature of influencing factors, including weather conditions, traffic patterns, seasonal variations, and geographical characteristics. In addition, issues such as incomplete or noisy data, limited real-time availability, and the difficulty of selecting suitable prediction models reduce forecasting accuracy. These challenges highlight the need for an efficient, localized, and real-time air pollution monitoring and prediction system. To address these issues, this system proposes a low-cost air pollution prediction system using an ESP32 microcontroller integrated with MQ-135, DHT11, and PM2.0 sensors. The system continuously monitors harmful gases, particulate matter, temperature, and humidity, and transmits the collected data wirelessly for further processing. Machine learning techniques are applied to analyze historical and real-time data to predict future air quality levels and classify them according to standard AQI categories. The predicted results are displayed on a web or mobile dashboard, with alerts generated when pollution exceeds safe limits. This system supports early warning, effective air quality management, and smart city development while improving public awareness and public health protection. Key Words: Esp32 Mq135,Dht11,Pm2.0 dust sensor ,I2C Lcd16x2 ,Zero PCB,Jumper wires

Objective: To investigate the effects of pre-pregnancy and prenatal exposure to six major air pollutants (CO, NO2, O3, PM10, PM2.5, SO2) on birth weight in newborns and identify critical exposure windows. Methods: This retrospective cohort study analyzed data from 1 561 mother-infant pairs who delivered at Taiyuan Central Hospital between January 2020 and December 2023, combined with contemporaneous air pollution monitoring records. This study used the geographic information system (GIS) technology to evaluate the individual air pollutants exposure level of pregnant women, and calculated the average pollutant concentrations for four stages: preconception (the 12 weeks before conception), first trimester (weeks 1-13), second trimester (weeks 14-27) and third trimester (weeks 28-37). Multiple linear regression models were used to assess the associations between air pollutant exposure and birth weight at different pregnancy stages. The distributed lag nonlinear model (DLNM) was further constructed to characterize the nonlinear exposure-lag-response relationships, identify sensitive windows, and examine sex differences. Results: Preliminary multiple linear regression showed that third-trimester exposures to PM2.5(β^=-43.00, 95%CI:-79.40- -6.68), PM10(β^=-35.00, 95%CI:-66.00- -4.07), NO2(β^=-35.50, 95%CI:-66.90- -4.08) and SO2(β^=-28.80, 95%CI:-52.70- -4.97) were negatively associated with birth weight (all P<0.05), but these associations disappeared after full adjustment for covariates. DLNM analysis revealed exposure-response relationships for CO, O3, PM10, and SO2 on birth weight, with distinct critical exposure windows: CO (weeks 2-13, 14-20), O3 (weeks 4-13, 14-24), PM10 (weeks 22-27, 28-37), and SO2 (weeks 11-13, 14-27 and 28-37). Among these, the largest effect were observed for CO at gestational week 12, O3 at week 13, and for both PM10 and SO2 at week 37. The effect estimates and their 95% confidence intervals were -37.12(-62.78- -11.45), -3.61(-6.45- -0.77), -5.01(-8.98- -1.04) and -8.31(-12.07- -4.56), all P<0.05. Gender differences in pollutant effects were observed: male newborns were more sensitive to SO2 across multiple stages, to CO in mid-pregnancy, and to PM10 in late-pregnancy; female newborns were more susceptible to PM10 in the preconception and early-to-mid-pregnancy periods, to O3 in early-to-mid pregnancy, and to SO2 in late pregnancy. Conclusion: Exposure to air pollutants during pregnancy is associated with reduced birth weight in newborns. Different pollutants have specific exposure windows, and gender differences exist. This finding provides important scientific evidence for preventing adverse pregnancy outcomes and developing intervention strategies to improve neonatal health.

Cancer therapies have well-documented adverse effects on cardiovascular and respiratory health which could increase cancer survivors' susceptibility to poor air quality. We describe the risk of cardiovascular and respiratory healthcare visits following acute air pollution exposures among adolescent and young adult (AYA) cancer survivors. We identified all Utah AYAs diagnosed at 15-39 years of age with thyroid, melanoma, lymphoma, breast, or testicular cancer from 1998-2016 (N=8,016). AYAs were linked by residential location to daily particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) for 2000-2016. Case-crossover models in a distributed lag non-linear model framework estimated odds ratios (ORs) and 95% confidence intervals (95% CIs) for cardiovascular and respiratory healthcare visits with exposure over the 6 days preceding an event, adjusting for temperature and relative humidity. A total of 3,143 AYAs (39%) experienced ≥1 respiratory/cardiovascular event. O3 was associated with cardiovascular events (ORlag4=1.08, 95% CI: 1.02-1.14; ORlag5=1.05, 95% CI:1.01-1.09) and NO2 was associated with respiratory events (ORlag1=1.05, 95% CI: 1.01-1.10). Breast cancer survivors showed elevated risk for cardiovascular and respiratory visits; melanoma, lymphoma, and testis survivors displayed increased risk for cardiovascular visits. O3 was linked with elevated risk of cardiovascular events while respiratory events were associated with NO2. Associations between air pollutants and healthcare visits varied by primary diagnosis site, indicating that susceptibility to air pollution could differ due to cancer-specific treatment factors. AYAs face increased risk for cardiovascular and respiratory events with exposure to ambient air pollution and may benefit from interventions to reduce exposures.

Urban expansion escalates both light and air pollution, posing significant threats to environmental health and energy sustainability. This study presents a spatiotemporal analysis of light pollution in Antalya, Turkey, and explores its relationship to urban air quality. Using ground-based measurements from a sky quality meter (SQM), clouds masks from EUMETSAT, and satellite data from NASA's Black Marble, a comprehensive analysis of night sky brightness and its long-term trends was conducted from 2012 to 2023. Analysis of annual composites identified specific development projects, such as a new hospital and airport expansion, as major sources of increased brightness, with total radiance consistently rising across all districts, reflecting urban sprawl. The relationship between particulate matter (PM2.5) and satellite-observed radiance was examined using data from four ground-based air quality monitoring stations. Despite filtering for clear-sky conditions and moonlight, no statistically significant correlation was found on monthly or daily timescales. This null result likely reflects the decoupling between surface-based PM2.5 measurements and the vertically integrated aerosol column influencing satellite-observed radiance. Upward radiant flux was also quantified to estimate a lower bound in terms of electric cost. Our findings highlight the impact of rapid urbanization on light pollution and provide a baseline for developing targeted mitigation strategies, while indicating that the interplay with air quality in this region is complex.

Physical inactivity is a major health risk worldwide, while walking is one of the most accessible forms of exercise that improves public health and supports sustainable urban mobility. Yet the combined and nonlinear effects of the built environments and seasonal climate on exercise walking in high-density cities remain insufficiently explored. This study aims to uncover these relationships and provide insights for health-oriented and climate-adaptive urban planning. Crowdsourced walking trajectory data were analyzed for three representative high-density Chinese cities, Beijing, Wuhan, and Guangzhou, covering both summer and winter. A comprehensive variable system was established, incorporating built environments, seasonal climate, and socioeconomic factors. A geographically weighted extreme gradient boosting model was developed with Bayesian optimization and cross-validation to improve robustness. Interpretability was achieved through Shapley Additive Explanations, partial dependence plots, and clustering analysis to identify global and local drivers of walking activity. The geographically weighted extreme gradient boosting model outperformed traditional regression and other machine learning models in prediction accuracy. Walking trajectories showed clear spatial clustering, with central urban cores as hotspots, and seasonal differences most pronounced in Beijing. Walk Score was consistently the most stable and influential factor across cities and seasons. Among climatic variables, air quality and temperature had the strongest impacts, particularly in winter. Variables exhibited three types of nonlinear responses: sustained growth (such as Walk Score and pedestrian street length), threshold-sensitive (such as intersection density and population density), and fluctuating patterns (such as air quality and housing prices). Local cluster analysis revealed three context-specific patterns: environment-driven areas such as parks and campuses, function-driven commercial centers, and structurally imbalanced or transitional zones. Exercise walking in high-density cities is shaped by both seasonal climate variability and spatial heterogeneity of the built environments. Improving pedestrian infrastructure, managing density thresholds, and implementing climate sensitive design can mitigate adverse weather impacts and foster year-round walking. Tailored strategies, including enhancing microclimate resilience in ecological zones, optimizing density and functional mix in commercial districts, and restructuring fragmented large blocks, are essential to create pedestrian friendly, health oriented, and climate adaptive cities.

The extensive availability of electricity and hydrogen in Texas provides enormous potential for adopting alternative energy for transportation. The operational phase of alternative energy infrastructure is an essential element in its environmental impact assessment and has not been evaluated up to this point, especially in terms of its utilization and vehicle mix. This study aims to evaluate the environmental impacts of alternative energy options, including fast/slow electric vehicle charging and hydrogen refueling at charging/refueling stations. The AFLEET Charging and Fueling Infrastructure (CFI) Emissions Tool was used to analyze the burden reductions in life cycle GHGs and air pollutants. The station operation considered infrastructure type, utilization, and vehicle mix (LDVs and HDVs). High utilization of the station yielded more burden reductions. Fast-charging supply equipment resulted in higher GHG burden reductions compared to the slow counterpart (367% in moderate utilization). Elevated burden reductions were observed in GHGs, VOC, and CO pollutants with more LDVs. There was an increase in NOx burden reductions of approximately 5200 lb. (moderate utilization), while transiting from 100% LDV to 75% LDV. Increased burden reductions were noted in particulate matter for 50% LDV. Also, enhanced burden reductions were observed in SOx with more HDVs for EVs and equal vehicle mix in hydrogen. Increased GHG burden reductions were identified in SMR than electrolysis. These results recommend policy makers focus on maximized utilization of the new or existing infrastructure to reduce environmental loads.

In this study, ten different plant leaves were collected from different urban microenvironments in the Hyderabad metropolitan area to assess their potential as bioindicators of air pollution. The collected leaf samples were analysed for surface PM accumulation, total chlorophyll concentration, carotenoid content, ascorbic acid, leaf extract pH, relative water content, and air pollution tolerance index (APTI). Out of ten plants, Ficus religiosa, which is located near the roadside, showed the highest PM accumulation with 54 µg/cm2. Tecoma stans showed the highest APTI value of 7.45 and ascorbic acid content of 10 mg/g, exhibiting greater tolerance to pollution, as well as potential for urban greening and air quality enhancement. Senegalia caesia with low APTI showed excellent carotenoid concentration of 19.42 mg/g, indicating that the plant is pollutant-sensitive, but activates antioxidant defence in response to oxidative stress. Hierarchical clustering analysis revealed a decent grouping of plant species based on biochemical profiles, validating stress tolerance patterns and PM deposition. The study highlights how pollutant deposition is linked to plant health and suggests that plants with adaptive leaf traits are more capable of tolerating polluted conditions. The findings of the study suggest that sensitive plants like Senegalia caesia, Lantana camara L., can serve as effective bioindicators and passive monitors of pollution levels and urban greening initiatives, and tolerant plants like Wodyetia bifurcata and Tecoma stans may contribute to a sustainable solution for mitigating urban air quality and supporting long-term environmental planning in polluted urban ecosystems.

ABSTRACT Stroke risk may be affected by the level of development in a neighborhood, which may in turn affect access to health-promoting environmental features, or exposure to noise and air pollution. This study investigated development intensity as a potential influence on stroke risk, using data from the United States Geological Survey National Land Cover Database (NLCD). We tested associations between development intensity and incident stroke among 25,330 adult participants in the REasons for Geographic And Racial Differences in Stroke study. We measured development intensity in 8-km network buffers around participants’ residential locations (34,368 locations). A proportional hazards model controlled for demographic characteristics and stroke risk factors. Approximately half (52.0%) of the participants were 45 to 64years of age, 54.9% were female, and 40.2% reported their race as Black. Median time in the study was 10.7 years. Average development intensity at baseline was −2.9. During the 2003 to 2017 study period, 1319 participants (5.2%) had an incident stroke. Exposure to greater development intensity was associated with reduced incident stroke risk (HR 0.975, 95% CI: 0.95-0.99). This study demonstrated the influence of development intensity on stroke risk. Further work is needed to understand the specific environmental features by which development intensity affects stroke risk.

Background Air pollution by fine particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM2.5) is the main environmental risk factor associated with diseases of the circulatory system (DCS), ischemic heart disease (IHD), and cerebrovascular diseases (CBVD). Objective To estimate mortality rates from DCS, IHD, and CBVD (2000-2019) among residents of the 164 neighborhoods of Rio de Janeiro, according to PM2.5 levels. Methods This retrospective ecological study used georeferenced satellite data classified into three PM2.5 levels and mortality records from the Department of Information and Informatics of the Unified Health System for DCS, IHD, and CBVD among individuals of both sexes aged ≥ 20 years from 2000 to 2019. Age-adjusted mortality rates per 1,000 inhabitants were calculated, and comparative statistical analyses were performed by sex, PM2.5 level, and age group (5% significance). Results Approximately 91% of the 4.7 million residents (≥ 20 years) live in areas with high or extreme PM2.5 pollution. Deaths occurred up to 3.4 years earlier among men living in highly polluted areas compared with those in moderately polluted areas. The highest DCS mortality rates were observed in neighborhoods with high and extreme pollution (female = 3.9 ± 1.7; 95% CI = 3.5-4.2; male = 4.6 ± 2.1; 95% CI = 4.1-4.9), particularly in individuals aged ≥ 70 years. Significant associations were found between mortality rates and pollution levels for DCS (p = 0.019), IHD (p = 0.025), and CBVD (p = 0.002) in the 50-69-year age group when comparing moderately and extremely polluted areas. Intermediate/high social vulnerability was identified in 71% of neighborhoods, with an increasing socioenvironmental gradient linking higher vulnerability to higher PM2.5 concentrations (R = 0.354; p = 0.001). Conclusion Mean PM2.5 concentrations in the neighborhoods of Rio de Janeiro exceeded the World Health Organization’s recommended standard by a factor of four. Mortality from DCS is significantly higher and occurs earlier in areas with high or extreme levels of pollution.

Nail technicians face various hazards in their work environment, influenced by complex factors, involving not only nail technicians and owners but also customers. While significant research has focused on technicians' health and safety views, customers' perspectives remain underexplored. This study addresses this gap by assessing changes in customer attendance since COVID-19 and describing their perceptions of nail salon health and safety. Using an online survey, we analyzed responses from 428 participants. Over 50% of participants reported a change in their nail salon attendance since the beginning of the COVID-19 pandemic; 32% reported visiting more frequently, while 22% visited less often. Most customers expressed satisfaction with salon cleanliness (93%) and air quality (86%). Approximately half had no strong preference for technician glove (46%) or mask use (53%). In addition, 80% were unaware of products marketed as having fewer chemicals. Raising awareness among customers toward the vital of health and safety in nail salons-such as the benefits of technicians' glove and mask use, as well as the merits of safer product use-can protect both customers and nail technicians. This study suggests that customers may be important partners in enhancing workplace health and safety. Future research should explore customers' expectations regarding health and safety and their preferences for safer products, providing valuable insights for advocates and policymakers to effectively tailor educational programs/public health campaign for customers and improve health and safety standards in the beauty industry.

Abstract. Atmospheric aerosols significantly contribute to air pollution and influence atmospheric chemistry, impacting air quality and public health. Decrease in aerosols can hinder the radical uptake sink of HO2, and thus increase NOx and OH, and subsequently increase ozone levels. This study investigates the seasonal variations of PM10 and aerosol surface area and their effect on surface ozone levels in India, using the GEOS-Chem Chemical Transport Model for the years 2018 and 2022, two years with high and low simulated PM10 concentrations, respectively. The results reveal substantial seasonal variations in PM10 and aerosol surface area. In winter (DJF), higher PM10 and aerosol surface area in the Indo-Gangetic Plain (IGP) and western Central India (CI) result from biomass burning and industrial activity, while coastal regions show lower aerosol surface area. A decrease in aerosol surface area is seen during the pre-monsoon (March–April–May; MAM) and monsoon (June–July–August–Septmember; JJAS), followed by an increase in the post-monsoon (ON) season. As a result, aerosol-induced HO2 uptake during winter and post-monsoon suppress surface ozone by approximately 5–10 µg m−3 in 2022 when compared to that of 2018. In contrast, during monsoon in 2022, the decrease in aerosol surface area caused an ozone increase of 5–7.5 µg m−3 when compared to that of 2018. On average, this increase in surface ozone due to the decrease in aerosols can be mitigated by reducing anthropogenic NOx emissions by about 25 %–50 %. Thus, we recommend integrated strategies addressing aerosols, precursor emissions and regional meteorology to combat ozone pollution.

Harnessing deep learning for air pollution forecasting: trends, techniques, and future prospects

Air pollution remains a significant public health challenge in Indonesia, driven by rapid industrialization and urbanization. Key pollutants such as NO₂ and SO₂ are strongly linked to respiratory conditions, yet comprehensive national evidence on their age-specific impacts remains limited. This study aims to examine the causal effects of NO₂ and SO₂ exposure on the incidence of acute respiratory infections (ARI), pneumonia, and asthma in Indonesia, with a particular focus on differences across age groups. This study employs a quasi-experimental design using district-level pollution data and individual health data from Riskesdas 2018. Analysis was conducted via multiple linear regression, coefficient stability testing, and IPW to estimate robust causal associations. Age-stratified analysis was performed across five groups: 0–4, 5–17, 18–49, 50–74, and 75+ years. Results show that NO₂ exhibits a strong positive association with ARI incidence, particularly among children aged 0–4 and 5–17 years, and is linked to asthma in adults aged 18–49. SO₂ shows significant positive effects on ARI among older adults (50–74 years) and on asthma in those aged 75 and above. However, unexpected negative or non-significant relationships were found between NO₂ and pneumonia/asthma, and between SO₂ and certain outcomes, likely reflecting data constraints and unobserved confounders. In conclusion, this study reveals age-specific pollutant–health relationships and underscores the need for targeted air quality interventions. Recommendations include strengthening monitoring systems, implementing pollutant-specific warnings, and integrating environmental–health data to support evidence-based policies and protect vulnerable groups.

Far ultraviolet-C (UVC) is an emerging, flexible technology for indoor air disinfection with the potential to reduce airborne transmission of pathogens while maintaining safety for human tissues. Despite its high efficacy to neutralize a wide range of pathogens and safety for human tissues, implementation of far UVC is hampered by regulatory gaps, consumer uncertainty, and unanswered research questions surrounding the formation and interaction of generated ozone and volatile oxidative byproducts. This commentary describes targeted recommendations for both epidemic-where rapid far-UVC deployment and ability to counter a wide variety of pathogens in balanced with potential environmental impacts on the indoor environment-and long term implementation scenarios, highlighting the need for human health risk studies, regulatory guidance for fa- UVC devices, and real-world cost benefit analyses, which consider the tradeoffs long term of far UVC and germicidal ultraviolet implementation. Far-UVC technologies demonstrate an exciting opportunity to promote the benefits of germicidal UV disinfection to more indoor spaces. More research is needed, however, to ensure its safe and equitable use and development. This work was in part informed by a workshop held by the Johns Hopkins Center for Health Security, which convened experts from academia, government, and industry to evaluate the scientific and policy considerations for far UVC, comparing the new technology to traditional germicidal ultraviolet.

The rapid urbanization of cities has exacerbated traffic congestion, resulting in significant environmental impacts, including elevated greenhouse gas emissions and deteriorating air quality. Traffic management systems, while effective in many contexts, often fail to consider the ecological and dynamic complexities of modern urban environments. This paper introduces MM-STMAP, a framework for urban traffic management that integrates multi modal perception with deep reinforcement learning. The approach utilizes a spatio temporal graph convolutional network to model intricate traffic patterns across diverse urban environments, while incorporating real-time environmental data, including meteorological factors, to address the ecological limitations of traditional traffic systems. A linear attention mechanism is employed to optimize computational efficiency in processing large-scale, dynamic traffic data, thereby enhancing both operational performance and energy consumption. The multi agent reinforcement learning structure governs the coordination of traffic signals across intersections, achieving a dual optimization of reduced vehicular delays and minimized emissions. Empirical evaluations on major metropolitan datasets demonstrate that MM-STMAP outperforms existing traffic management methods and significantly enhances traffic flow efficiency. The model's ability to integrate heterogeneous data streams spanning traffic sensors and environmental reports enables a comprehensive and adaptive approach to urban mobility, supporting the development of sustainable smart city infrastructure.

Assessing transboundary atmospheric pattern changes and local to national air quality impacts of regional fine dust by using novel NASA satellite data a case study of Sarpol e Zahab Iran

Abstract. To what extent the new particle formation (NPF) contributed to the cloud condensation nuclei (CCN) remained unclear, especially at the boundary layer top (BLT) in polluted atmosphere. Based on measurements at a mountain-top background site in southeastern China during spring 2024, this study systematically investigates the nucleation mechanism and subsequent growth dynamics of NPF events under contrasting air masses, and quantifies their role as a source of CCN. Eight NPF events were observed, and three of them occurred in the polluted conditions (NPF-P) which associated with regional transportation while the rest five events appeared in the clean conditions (NPF-C). The average formation rate (J2.5: 2.4 cm-3s-1 vs. 0.7 cm-3s-1) and growth rate (GR: 6.8 nm h−1 vs. 5.5 nm h−1) were significantly higher in NPF-P events than in NPF-C events, alongside elevated concentrations of sulfuric acid and ammonia. The correlation between log J3 and [H2SO4], as well as theoretical simulations with the MALTE_BOX model, indicates that the enhanced nucleation in polluted conditions can be attributed to the participation of ammonia in stabilizing sulfuric acid-based clusters. In addition, much higher CCN enhancement factor was observed in NPF-P (EFCCN: 1.6 vs. 0.7 in NPF-C) due to the regional transported of anthropogenic pollutants from the urban cluster regions and their secondary transformation under enhanced atmospheric oxidation capacity. Furthermore, the duration of NPF-to-CCN conversion was quantified using a “Time Window (τ)”, revealing that polluted conditions accelerated the conversion by 17.0 % (τ = 16.4 h vs. 19.8 h). Nitrate played an important role in maintaining a rapid particle growth rate, thereby shortening τ and enhancing CCN production from NPF – a process that can ultimately influence cloud microphysical properties by increasing the potential cloud droplet number concentration. These findings reveal that polluted air masses enhance both the efficiency and speed of CCN production at the BLT through elevated atmospheric oxidation capacity.

The pervasive toxicity of active aluminum (Al3+) in acidic red soils threatens agroecosystem sustainability, with conventional chemical stabilizers facing cost and secondary pollution constraints. This study evaluated rice husk/sawdust and their pyrolysis-derived biochar as stabilizers, focusing on microbial synergy. Results showed 3% rice husk biochar (RB) achieved 22.1 ± 1.1% stabilization efficiency within 180 days, outperforming sawdust biochar (12.1 ± 0.8%) and raw biomass. Biochar’s alkalinity and porosity created neutral niches, enriching denitrifiers (Thiobacillus, Arthrobacter, Thermomonas) that elevated pH, promoted Al(OH)3 precipitation, and enhanced oxygen-containing functional groups. This work valorizes agricultural waste for long-term Al3+ toxicity mitigation.

Abstract Synthetic dyes are extensively used in the textile industry and represent a major source of environmental pollution due to the discharge of colored effluents into aquatic ecosystems. Conventional physical and chemical treatment methods are often ineffective in completely removing these dyes and may generate secondary pollutants or increase toxicity. Consequently, environmentally friendly and sustainable biological alternatives are required. This study aimed to isolate, characterize, and evaluate indigenous fungi from dye-contaminated environments for the decolorization of Remazol Red (RR) dye. Fungal isolates were obtained from the Cibuluh River, Cisadane River, and textile industry wastewater treatment plants. Initial screening was performed on solid media containing RR dye, resulting in 45 fungal isolates, of which 15 demonstrated strong resistance and decolorization capability at elevated dye concentrations. Five promising isolates (TB1, TB3, TB4, SB1, and SB2) were further evaluated in liquid media containing RR at concentrations ranging from 100 to 1000 mg/L. Among the tested isolates, TB1, TB3, and TB4 exhibited the highest decolorization efficiencies, achieving nearly complete dye removal at lower concentrations and maintaining substantial activity even at higher levels within five days of incubation. Visual observations and spectrophotometric analyses confirmed effective dye removal. Morphological and molecular identification based on ITS sequencing revealed that SB1 was closely related to Aspergillus tamarii, SB2 to Aspergillus awamori, TB1 to Aspergillus niger, TB4 to Trametes polyzona, and TB3 to Cladosporium cladosporioides. This study highlights the strong potential of indigenous fungi as cost-effective and sustainable agents for textile dye bioremediation.

Abstract Active commuting offers important environmental, economic, and health benefits, yet urban inequalities shape who can access these benefits. This paper analyzes the relationship between pedestrian mobility and mental well-being by examining how environmental and socioeconomic attributes associated with well-being—tree density, vegetation cover (NDVI), air and noise pollution, property values, and safety—are spatially distributed in two contrasting urban contexts: São Paulo (Global South) and Lisbon (Global North). Using a GIS-based framework, we constructed 15-minute walking isochrones for 68,807 pedestrians and integrated environmental and socio-demographic indicators through spatial overlay, correlation, and clustering. Results show that conditions associated with mental well-being are unevenly distributed: greener, safer, and less polluted areas are disproportionately linked to higher-income and more educated groups, while disadvantaged populations face greater exposure to risks such as air pollution and unsafe pedestrian environments. Spatial clustering identified four distinct clusters in São Paulo and three in Lisbon, highlighting how socio-spatial inequalities structure active commuting conditions in both cities. Conceptually, the study demonstrates that opportunities for mental well-being are shaped by the spatial organization of urban environments. Methodologically, it shows how GIScience can operationalize individual exposures and reveal hidden intra-urban disparities, providing a transferable framework for comparative research and actionable evidence for policies aimed at equitable and healthier cities.