Quantifying Prescribed-Fire Smoke Exposure Using Low-Cost Sensors and Satellites: Springtime Burning in Eastern Kansas.

Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM2.5). The Flint Hills (FH) region of Kansas experiences extensive prescribed burning each spring (March-May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM2.5 concentrations to the prescribed burning in the FH. To determine PM2.5 increases from local burning, we used low-cost PM2.5 sensors (PurpleAir) and satellite observations. The FH were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24-hr median PM2.5 to increase by 3.0-5.3 µg m-3 (based on different estimates) on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. The FH region was the most impacted by smoke PM2.5 compared to other regions of Kansas, as observed in satellite products and in situ measurements. Additionally, our study found that hourly PM2.5 estimates from a satellite-derived product aligned with our ground-based measurements. Satellite-derived products are useful in rural areas like the FH, where monitors are scarce, providing important PM2.5 estimates.

Investigating the effectiveness of a new indoor ventilation model in reducing the spread of disease: A case of sports centres amid the COVID-19 pandemic.

The ventilation of buildings is crucial to ensure indoor health, especially when demanding physical activities are carried out indoors, and the pandemic has highlighted the need to develop new management methods to ensure adequate ventilation. In Spain, there are no specific ventilation regulations to prevent the spread of pathogens such as the coronavirus. Therefore, it is necessary to have a theoretical tool for calculating occupancy to maintain sports facilities in optimal safety conditions. The proposed theoretical method is based on the analysis of mathematical expressions from European standardisation documents and uses the concentration of CO2 as a bioeffluent. It is also based on the concept of background and critical concentration, which allows its application to be extrapolated to future crises caused by pathogens. This study presents a unique and novel dataset for sports centres. For this purpose, the calculation methods were applied to the data set provided by Mostoles City Council, Spain, during the pandemic years with the highest incidence of COVID-19, when the government introduced the assimilation of COVID-19 sick leave to occupational accidents. The data on this type of sick leave provided by the City Council correspond to the period between March 2020 and February 2022. Similarly, the data on the average use of sports facilities by activity, provided by the Sports Department, correspond to the years 2020 and 2021. In this way, it was possible to verify the effectiveness in preventing the spread of any type of coronavirus. In conclusion, the implementation of a theoretical occupancy calculation method based on the concentration of carbon dioxide as a bioeffluent can be an effective tool for the management of future crises caused by pathogens or hazardous chemicals in the air, and demonstrated its effectiveness in sports centres such as gyms, sports fields, and indoor swimming pools during the COVID-19 pandemic.

Feasibility of residential air quality monitoring to address asthma outcomes.

Improving asthma outcomes for underserved populations can be addressed through interventions to improve indoor air quality (IAQ). New protocol for measuring IAQ and health outcomes are imperative given advances in IAQ monitoring technology and challenges in conducting intervention research in homes. In this pilot study HEPA air purifiers and HEPA vacuum cleaners were provided to five homes with children with asthma. For 6 weeks, eight common components of air quality were measured using a low-cost multi-channel air quality monitoring device, with data conveyed directly from participant homes via Wi-Fi connection. In conjunction with periodic surveys on asthma control, impact of asthma on quality of life and intervention compliance, outcomes compared IAQ, home characteristics, and asthma-related measures. This pilot study demonstrates the feasibility of a protocol to evaluate a dual component intervention to improve IAQ in homes, as measured with a low-cost air quality monitoring device.

Quantifying the impact of urban road traffic on air quality: activity pre-pandemic and during partial and full lockdowns.

The impact of partial and full COVID lockdowns in 2020 on vehicle miles traveled (VMT) in Kuwait was estimated using data extracted from the Directions API of Google Maps and a Python script running as a cronjob. This approach was validated by comparing the predictions based on the app to measuring traffic flows for 1 week across four road segments considered in this study. VMT during lockdown periods were compared to VMT for the same calendar weeks before the pandemic. NOx emissions were estimated based on VMT and were used to simulate the spatial patterns of NOx concentrations using an air quality model (AERMOD). Compared to pre-pandemic periods, VMT was reduced by up to 25.5% and 42.6% during the 2-week partial and full lockdown episodes, respectively. The largest reduction in the traffic flow rate occurred during the middle of these 2-week periods, when the traffic flow rate decreased by 35% and 49% during the partial and full lockdown periods, respectively. The AERMOD simulation results predicted a reduction in the average maximum concentration of emissions directly related to VMT across the region by up to 38%, with the maximum concentration shifting to less populous residential areas as a result of the lockdown.

Exploring the contribution of nature-based solutions for environmental challenges in the Netherlands.

Nature-based solutions (NbS) offer a promising and sustainable approach to addressing multiple environmental challenges, including climate change, pollution, and biodiversity loss. Despite the potential of NbS, their actual effectiveness in solving these challenges remains uncertain. Therefore, this study evaluates the contribution of NbS implemented in a nature-inclusive scenario for six environmental challenges and associated policy targets in the Netherlands. Fifteen different NbS were applied in the scenario in urban, agricultural, aquatic, and protected nature areas, with measures like flower field margins, green roofs, groundwater level management, and river restoration. The spatially-explicit Natural Capital Model was used to quantify the effectiveness of all applied NbS at a national-scale. Results show NbS significantly contribute to simultaneously solving all six assessed environmental challenges. The most significant impact was seen in improving the quality of water bodies (+34 %), making agriculture more sustainable (+24 %), and protecting and restoring biodiversity (+22 %). The contribution of NbS to address the quality of the living environment (+13 %), climate change (+10 %), and the energy transition was less effective (+2 %). Furthermore, NbS can help to achieve sectoral policy targets at the global, EU, and national levels, including those related to the Birds Habitats Directives, carbon emission, and pesticide reduction targets. This study highlights the potential of NbS to effectively address multiple environmental challenges, although they do not provide a complete solution, and suggests that future research could focus on identifying even more effective ways to implement NbS, and to mainstream their use in policy and practice.

Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001-2018).

Tropospheric nitrogen dioxide (NO2) poses a serious threat to the environmental quality and public health. Satellite NO2 observations have been continuously used to monitor NO2 variations and improve model performances. However, the accuracy of satellite NO2 retrieval depends on the knowledge of aerosol optical properties, in particular for urban agglomerations accompanied by significant changes in aerosol characteristics. In this study, we investigate the impacts of aerosol composition on tropospheric NO2 retrieval for an 18 year global data set from Global Ozone Monitoring Experiment (GOME)-series satellite sensors. With a focus on cloud-free scenes dominated by the presence of aerosols, individual aerosol composition affects the uncertainties of tropospheric NO2 columns through impacts on the aerosol loading amount, relative vertical distribution of aerosol and NO2, aerosol absorption properties, and surface albedo determination. Among aerosol compositions, secondary inorganic aerosol mostly dominates the NO2 uncertainty by up to 43.5% in urban agglomerations, while organic aerosols contribute significantly to the NO2 uncertainty by -8.9 to 37.3% during biomass burning seasons. The possible contrary influences from different aerosol species highlight the importance and complexity of aerosol correction on tropospheric NO2 retrieval and indicate the need for a full picture of aerosol properties. This is of particular importance for interpreting seasonal variations or long-term trends of tropospheric NO2 columns as well as for mitigating ozone and fine particulate matter pollution.

Characteristics and health implications of fine particulate matter near urban road site in Islamabad, Pakistan.

The escalating issue of air pollution has become a significant concern in urban regions, including Islamabad, Pakistan, due to the rise in air pollutant emissions driven by economic and industrial expansion. To gain a deeper understanding of air pollution, a study was conducted during winter 2022-2023, assessing physical, chemical, and biological factors in Islamabad. The findings revealed that the average concentration of fine particulate matter (PM2.5) was notably greater than the World Health Organization (WHO) guidelines, reaching 133.39 µg/m³. Additionally, the average concentration of bacteria (308.64 CFU/m³) was notably greater than that of fungi (203.55 CFU/m³) throughout the study. Analytical analyses, including SEM-EDS and FTIR, showed that the PM2.5 in Islamabad is composed of various particles such as soot aggregates, coal fly ash, minerals, bio-particles, and some unidentified particles. EF analysis distinguished PM2.5 sources, enhancing understanding of pollutants origin, whereas Spearman's correlation analysis elucidated constituent interactions, further explaining air quality impact. The results from the Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-OES) indicated a gradual increase in the total elemental composition of PM2.5 from autumn to winter, maintaining high levels throughout the winter season. Furthermore, a significant variation was found in the mass concentration of PM2.5 when comparing samples collected in the morning and evening. The study also identified the presence of semi-volatile organic compounds (SVOCs) in PM2.5 samples, including polycyclic aromatic hydrocarbons (PAHs) and phenolic compounds, with notable variations in their concentrations. Utilizing health risk assessment models developed by the US EPA, we estimated the potential health risks associated with PM2.5 exposure, highlighting the urgency of addressing air quality issues. These findings provide valuable insights into the sources and composition of PM2.5 in Islamabad, contributing to a comprehensive understanding of air quality and its potential environmental and health implications.

Indoor sulfur dioxide prediction through air quality modeling and assessment of sulfur dioxide and nitrogen dioxide levels in industrial and non-industrial areas.

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant.

Unlocking the nanoparticle emission potential: a study of varied filaments in 3D printing.

This study investigates nanoparticle emission during 3D printing processes, assessing various filament materials' impact on air quality. Commonly used 3D printers, including both filament and resin-based types, were examined. The study's scope encompasses diverse filament materials like ABS (acrylonitrile butadiene styrene), PLA (polylactic acid), PETG (polyethylene terephthalate glycol), ASA (acrylonitrile styrene acrylate), TPU (thermoplastic polyurethane), PP (polypropylene), nylon, and wood-based variants, alongside three types of resins. The research delves into the relationship between the type of material and nanoparticle emissions, emphasizing temperature's pivotal role. Measurement instruments were employed for nanoparticle quantification, including an engine exhaust particle sizer spectrometer, condensation particle counter, and nanozen dust counters. Notably, results reveal substantial variations in nanoparticle emissions among different filament materials, with ASA, TPU, PP, and ABS showing considerably elevated emission levels and characteristic particle size distribution patterns. The findings prompt practical recommendations for reducing nanoparticle exposure, emphasizing printer confinement, material selection, and adequate ventilation. This study offers insights into potential health risks associated with 3D printing emissions and provides a basis for adopting preventive measures.

Enhancing removal of air contaminants in existing aircraft cabins by optimizing supply air direction based on Re-field synergy and Bayesian optimization.

There are a large number of airplanes currently being operated, in which the ventilation system needs to be improved to more effectively remove air contaminants. A potential approach is to adjust the supply air directions with the use of simple airflow deflectors. This study proposed a method for optimizing the supply air direction of ventilation in aircraft cabins based on the Re-field synergy index and Bayesian optimization. A validated numerical model was used to calculate the air distribution and air contaminant transport in a single-row single-aisle aircraft cabin to obtain the Re-field synergy values. The Bayesian optimization approach was used to identify the supply air direction which maximizes the Re-field synergy, namely, maximizes the mass transfer effectiveness. Finally, the air contaminant transport in a 7-row single-aisle aircraft cabin with the optimized supply air direction was evaluated to demonstrate the enhancement of ventilation performance. The results show that the proposed method based on the Re-field synergy index and Bayesian optimization can efficiently optimize the supply air direction in order to enhance the air contaminant removal in aircraft cabins. In the 7-row single-aisle aircraft cabin, the optimized supply air direction can reduce the average air contaminant concentration in the breathing zone of the passengers by up to 23 %.

A comprehensive review of urban vegetation as a Nature-based Solution for sustainable management of particulate matter in ambient air.

Air pollution is one of the most pressing environmental threats worldwide, resulting in several health issues such as cardiovascular and respiratory disorders, as well as premature mortality. The harmful effects of air pollution are particularly concerning in urban areas, where mismanaged anthropogenic activities, such as growth in the global population, increase in the number of vehicles, and industrial activities, have led to an increase in the concentration of pollutants in the ambient air. Among air pollutants, particulate matter is responsible for most adverse impacts. Several techniques have been implemented to reduce particulate matter concentrations in the ambient air. However, despite all the threats and awareness, efforts to improve air quality remain inadequate. In recent years, urban vegetation has emerged as an efficient Nature-based Solution for managing environmental air pollution due to its ability to filter air, thereby reducing the atmospheric concentrations of particulate matter. This review characterizes the various mitigation mechanisms for particulate matter by urban vegetation (deposition, dispersion, and modification) and identifies key areas for further improvements within each mechanism. Through a systematic assessment of existing literature, this review also highlights the existing gaps in the present literature that need to be addressed to maximize the utility of urban vegetation in reducing particulate matter levels. In conclusion, the review emphasizes the urgent need for proper air pollution management through urban vegetation by integrating different fields, multiple stakeholders, and policymakers to support better implementation.

Low-cost interventions to improve ventilation in long-term care facilities.

Residents of long-term care facilities are particularly vulnerable to communicable diseases. Low-cost interventions to increase air exchange rates (AERs) may be useful in reducing the transmission of airborne communicable diseases between long-term care residents and staff. In this study, carbon dioxide gas was used as a tracer to evaluate the AER associated with the implementation of low-cost ventilation interventions. Under baseline conditions with the room's door closed, the mean AER was 0.67 ACH; while baseline conditions with the door open had a significantly higher mean AER of 3.87 ACH (p < 0.001). Subsequently opening a window with the door open increased mean AER by 1.49 ACH (p = 0.012) and adding a fan in the window further increased mean AER by 1.87 ACH (p < 0.001). Regression analyses indicated that the flow rate of air entering through the window, both passively and through the use of a fan, was significantly associated with an increase in AER (p < 0.001). These results indicate that low-cost interventions that pull outside air into resident rooms were effective in improving the air exchange rates in these facilities. While implementation of these interventions is dependent on facility rules and isolation requirements of residents with airborne communicable diseases, these interventions remain viable options for long-term care facilities to improve resident room ventilation without requiring costly ventilation system upgrades.

Temporal pattern classification of PM2.5 chemical compositions in Seoul, Korea using K-means clustering analysis.

Particulate matter with a diameter ≤ 2.5 µm (PM2.5) is a complex mixture of particles with a variety of compositions potentially including sulfate ions (SO42-), nitrate ions (NO3-), ammonium ions (NH4+), organic and inorganic elemental carbon, and metals. Here, the temporal composition evolution of PM2.5 was analyzed to characterize its emission source, origin, and external influences. The concentrations of wintertime PM2.5 chemical compositions in Seoul, Korea during the period of 2012-2021 were classified into four representative clusters using a K-means cluster analysis method. Cluster 1 exhibited high concentrations of NO3- and NH4+ ions mainly due to the prevalence of emissions from domestic manure and fertilizer sources in the northeast. High concentrations of these two ions are conducive to generation of ammonium nitrate (NH4NO3) through atmospheric chemical reactions, resulting in relatively long-lasting high PM2.5 concentrations in Seoul. In cluster 2, high concentrations of SO42-, vanadium, and nickel were observed in frequent south-westerly winds, indicating the domestic influence of industrial facilities. Cluster 3 showed high concentrations of potassium ions and organic carbon, highlighting a pronounced external influence transported from China via prevailing westerly winds. Cluster 4 showed low PM2.5 concentrations accompanied by strong winds in warm environments, which are uncommon in winter. This study revealed that the air quality in Seoul, which was influenced by many factors, could be classified into four representative patterns. Our results provide insights into the emission sources, major influences, and responsible mechanisms of high PM2.5 concentrations in Seoul, which can help with air quality policies.

Temporal characteristics and health effects related to NO2, O3, and SO2 in an urban area of Iran.

This study reports on temporal variations of NO2, O3, and SO2 pollutants and their related health effects in urban air of Khorramabad, Iran using AirQ 2.2.3 software. Based on data between 2015 and 2021, hourly NO2, O3, and SO2 concentrations increase starting at 6:00 a.m. local time until 9:00 p.m., 3:00 p.m., and 7:00 p.m. local time, respectively, before gradually decreasing. The highest monthly NO2, O3, and SO2 concentrations are observed in October, August, and September, respectively. Annual median NO2, O3, and SO2 concentrations range between 17 ppb and 38.8 ppb, 17.5 ppb-36.6 ppb, and ∼14 ppb-30.8 ppb, respectively. Two to 93 days and 17-156 days between 2015 and 2021 exhibit daily concentrations of NO2 and SO2 ≤ WHO AQGs, respectively, while 187-294 days have 8-h maximum O3 concentrations ≤ WHO AQGs. The mean excess mortality ascribed to respiratory mortality, cardiovascular mortality, hospital admissions for COPD, and acute myocardial infraction are 121, 603, 39, and 145 during 2015-2021, respectively. O3 is found to exert more significant health effects compared to SO2 and NO2, resulting in higher cardiovascular mortality. The gradual increase in NO2 and possibly O3 over the study period is suspected to be due to economic sanctions, while SO2 decreased due to regulatory activity. Sustainable control strategies such as improving fuel quality, promoting public transportation and vehicle retirement, applying subsidies for purchase of electric vehicles, and application of European emission standards on automobiles can help decrease target pollutant levels in ambient air of cities in developing countries.

Integrated Portable and Stationary Health Impact-Monitoring System for Firefighters.

The multi-layered negative effects caused by pollutants released into the atmosphere as a result of fires served as the stimulus for the development of a system that protects the health of firefighters operating in the affected area. A collaborative network comprising mobile and stationary Internet of Things (IoT) devices that are furnished with gas sensors, along with a remote server, constructs a resilient framework that monitors the concentrations of harmful emissions, characterizes the ambient air quality of the vicinity where the fire transpires, adopting European Air Quality levels, and communicates the outcomes via suitable applications (RESTful APIs and visualizations) to the stakeholders responsible for fire management decision making. Different experimental evaluations adopting separate contexts illustrate the operation of the infrastructure.

Temporal variation of the PM2.5/PM10 ratio and its association with meteorological factors in a South American megacity: Metropolitan Area of Lima-Callao, Peru.

The Metropolitan Area of Lima-Callao (MALC) is a South American megacity that has suffered a serious deterioration in air quality due to high levels of particulate matter (PM2.5 and PM10). Studies on the behavior of the PM2.5/PM10 ratio and its temporal variability in relation to meteorological parameters are still very limited. The objective of this study was to analyze the temporal trends of the PM2.5/PM10 ratio, its temporal variability, and its association with meteorological variables over a period of 5 years (2015-2019). For this, the Theil-Sen estimator, bivariate polar plots, and correlation analysis were used. The regions of highest mean concentrations of PM2.5 and PM10 were identified at eastern Lima (ATE station-41.2 µg/m3) and southern Lima (VMT station-126.7 µg/m3), respectively. The lowest concentrations were recorded in downtown Lima (CDM station-16.8 µg/m3 and 34.0 µg/m3, respectively). The highest average PM2.5/PM10 ratio was found at the CDM station (0.55) and the lowest at the VMT station (0.27), indicating a predominance of emissions from the vehicular fleet within central Lima and a greater emission of coarse particles by resuspension in southern Lima. The temporal progression of the ratio of PM2.5/PM10 showed positive and highly significant trends in northern and central Lima with values of 0.03 and 0.1 units of PM2.5/PM10 per year, respectively. In the southern region of Lima, the trend was also significant, showcasing a value of 0.02 units of PM2.5/PM10 per year. At the hourly and monthly level, the PM2.5/PM10 ratio presented a negative and significant correlation with wind speed and air temperature, and a positive and significant correlation with relative humidity. These findings offer insights into identifying the sources of PM pollution and are useful for implementing regulations to reduce air emissions considering both anthropogenic sources and meteorological dispersion patterns.

Association between short- and medium-term exposure to air pollutants and depressive episode using comprehensive air quality index among the population in South Korea.

BACKGROUND: Currently, air pollution is suggested as a risk factor for depressive episodes. Our study aimed to consider multiple air pollutants simultaneously, and continuously evaluate air pollutants using comprehensive air quality index (CAI) with depressive episode risk. METHODS: Using a nationally representative sample survey from South Korea between 2014 and 2020, 20,796 participants who underwent health examination and Patient Depression Questionnaire-9 were included in the study. Six air pollutants (PM10, PM2.5, O3, CO, SO2, NO2) were measured for the analysis. Every air pollutant was standardized by air quality index (AQI) and CAI was calculated for universal representation. Using logistic regression, short- and medium-term exposure by AQI and CAI with the risk of depressive episode was calculated by odds ratio and 95 % confidence interval (CI). Furthermore, consecutive measurements of CAI over 1-month time intervals were evaluated with the risk of depressive episodes. Every analysis was conducted seasonally. RESULTS: There were 950 depressive episodes occurred during the survey. An increase in AQI for short-term exposure (0-30 days) showed higher risk of depressive episode in CO, while medium-term exposure (0-120 days) showed higher risk of depressive episode in CO, SO2, PM2.5, and PM10. During the cold season, the exposure to at least one abnormal CAI within 1-month intervals over 120 days was associated with a 68 % (95 % CI 1.11-2.54) increase in the risk of depressive episode. CONCLUSIONS: Short- and medium-term exposure of air pollution may be associated with an increased risk of depressive episodes, especially for cold season.

Decreased air quality shows minimal influence on peak summer attendance at forested Pacific West national parks.

Wildfires are increasing in duration and intensity across the United States' Pacific West region, resulting in heightened particulate matter from smoke in the atmosphere. Levels of peak particulate matter are concurrent to peak visitor attendance at National Parks, given seasonal alignment with summer vacation travel and heightened forest fire conditions. Particulate matter threatens visitor health and safety and contributes to poor visibility and a deteriorated visitor experience. To assess visitation response to diminished air quality, we utilized wildfire-generated particulate matter (PM2.5) data in conjunction with monthly attendance records for three ecoregions containing eight national parks in Washington, Oregon, and California from 2009 to 2019. We analyzed daily PM2.5 levels from data gridded at the 10 km scale for National Park Service units by Level III forest ecoregions within the National Park Service's Pacific West Unit. Data were then compared to normalized monthly visitation trends for each of the ecoregions using two statistical methods Kendall's Tau and Analysis of Variance (ANOVA) with post-hoc Tukey tests. Results demonstrate that attendance at these national parks does not decrease in response to increased PM2.5 levels. Instead, we see several statistically significant increases in attendance across these ecoregions during periods of reduced air quality. Of 115 shifts between air quality categories during the busy season of July to September, there are no significant decreases in attendance as air quality worsens. These findings suggest that visitors are willing to tolerate reduced air quality compared to other factors such as temperature or precipitation. Given that park units within each ecoregion feature diverse historical contexts, varied built environments, and unique ecological systems, our discussion specifically addresses managerial concerns associated with maintained high levels of visitation during suboptimal, and potentially dangerous, conditions. There is substantial need for specific, scalable approaches to mitigate adverse health and experiential impacts as visitors are exposed to increased risks during a range of exertional activities associated with diverse settings.

How urban growth dynamics impact the air quality? A case of eight Indian metropolitan cities.

Air pollution is a matter of great significance that confronts the sustainable progress of urban areas. Against India's swift urbanization, several urban areas exhibit the coexistence of escalating populace and expansion in developed regions alongside extensive spatial heterogeneity. The interaction mechanism between the growth of urban areas and the expansion of cities holds immense importance for the remediation of air pollution. Henceforth, the present investigation utilizes geographically weighted regression (GWR) to examine the influence of urban expansion and population growth on air quality. The examination will use a decade of data on the variation in PM2.5 levels from 2010 to 2020 in eight Indian metropolitan cities. The study's findings demonstrate a spatial heterogeneity between urban growth dynamics and air pollution levels. Urban growth and the expansion of towns demonstrate notable positive impacts on air quality, although the growth of infilling within expanding urban areas can significantly affect air quality. Given the unique trajectories of urban development in developing countries, this research provides many suggestions for urban administrators to foster sustainable urban growth.

Effects of surface-attached durations, nutrients, and relative humidity on the resuspension of bacteria during human walking.

Resuspension caused by human walking activities is an important source of indoor bioaerosols and has been associated with health effects such as allergies and asthma. However, it is unknown whether inhalation of resuspended bioaerosols is an important exposure pathway for airborne infection. Also, crucial factors influencing the resuspension of settled microbes have not been quantified. In this study, we experimentally investigated the resuspension of culturable bacteria from human-stepping on polyvinyl chloride (PVC) flooring under different conditions. We determined the bacterial resuspension emission factor (ER), a normalized resuspension parameter for the ratio of resuspended mass in the air to the mass of settled particles, for two common bacteria, Escherichia coli and Salmonella enterica. The investigation involved varying factors such as microbial surface-attached durations (0, 1, 2, and 3 days), the absence or presence of nutrients on flooring surfaces, and changes in relative humidity (RH) (35%, 65%, and 85%). The results showed that, in the absence of nutrients, the highest ER values for E. coli and S. enterica were 3.8 × 10-5 ± 5.2 × 10-6 and 5.3 × 10-5 ± 6.0 × 10-6, respectively, associated with surface-attached duration of 0 days. As the surface-attached duration increased from 0 to 3 days, ER values decreased by 92% and 84% for E. coli and S. enterica, respectively. In addition, we observed that ER values decreased with the increasing RH, which is consistent with particle adhesion theory. This research offers valuable insights into microbial resuspension during human walking activities and holds the potential for assisting in the assessment and estimation of risks related to human exposure to bioaerosols.