Experimental evaluation of a full-scale in-duct UV germicidal irradiation system for bioaerosols inactivation.

Bioaerosols control techniques, especially ultraviolet germicidal irradiation (UVGI) are gaining attention due to increasing needs for controlling of health risk caused by airborne biocontaminants. The effectiveness of a full-scale in-duct UVGI air disinfection system was investigated. One bacterium, a wild type Escherichia coli, and three fungal spores, Penicillium aragonense, Rhodotorula glutinis, and Cladosporium sp., were selected as test organisms and their inactivation under different conditions representative of a real application in HVAC systems were investigated. The results demonstrated that inactivation of airborne E. coli by the UVGI system was extremely effective, with >99.5 % of the input E. coli inactivated at a residence time lower of 0.36 s in the disinfection section. Airborne fungal spores were less susceptible to UV irradiation than E. coli. Under same conditions, viable counts reduction of P. aragonense, R. glutinis, and Cladosporium sp. spores were 53 %, 63 % and 73 %, respectively. The effect of UV light intensity, air flowrate and relative humidity were analyzed separately. A simplified model based on redefinition of the parameters in the classical inactivation kinetic equation was used to simulate the inactivation of airborne contaminants in the in-duct system under different conditions. The results showed that the simplified model was adequate to estimate disinfection efficacy of different bioaerosols by the UVGI system and that such in-duct systems can provide significant control of bioaerosols.

Air quality assessment of a mass deployment of microgrids.

Microgrids are emerging to mitigate the degradation in grid resiliency and reliability resulting from an increasing frequency of grid outages. Because microgrids incorporate a local source of power generation, the production of electricity is shifting from a centralized to distributed topology, thereby installing power generation resources and the concomitant emissions into heavily populated urban air sheds and residential communities. In this paper, the air quality and public health impacts of a mass deployment of microgrids in an urban air shed are assessed. Candidates to become microgrids are identified for both the near- and long-term deployment, and two microgrid scenarios are considered, differing by the 24/7 prime source of power: (1) combustion gas turbine (CGT)-based microgrids and (2) zero-emission FC-based microgrids complemented by solar PV and battery energy storage. Spatially and temporally resolved emissions from the microgrids are input to an air quality model and assessed for health impacts. The results show that (1) a mass deployment of CGT-based or FC-based microgrids in both the near- and long-term has a relatively small impact on air quality, (2) the health impacts are nonetheless significant for CGT-based microgrids due to the large and dense population of the area, and (3) disadvantaged communities are disproportionately impacted with the deployment of CTG-based microgrids. For example, near-term deployment of CGT-based microgrids results in an increase in the incidence of premature mortality (1 to 5 incidences per month) and an increase of $33 to $56 million per month in health costs. Deploying zero-emission FC-based microgrids mitigates the adverse health impact, prevents several incidences of premature mortality, and results in saving of ~$36M per month rather than a cost per month of ~$50M.

Association of Short-Term Increases in Ambient Fine Particulate Matter With Hospitalization for Asthma or COPD During Wildfire Season and Other Time Periods.

BACKGROUND: Short-term increases in air pollution are associated with poor asthma and COPD outcomes. Short-term elevations in fine particulate matter (PM2.5) due to wildfire smoke are becoming more common. RESEARCH QUESTION: Are short-term increases in PM2.5 and ozone in wildfire season and in winter inversion season associated with a composite of emergency or inpatient hospitalization for asthma and COPD? STUDY DESIGN AND METHODS: Case-crossover analyses evaluated 63,976 and 18,514 patients hospitalized for primary discharge diagnoses of asthma and COPD, respectively, between January 1999 and March 2022. Patients resided on Utah's Wasatch Front where PM2.5 and ozone were measured by Environmental Protection Agency-based monitors. ORs were calculated using Poisson regression adjusted for weather variables. RESULTS: Asthma risk increased on the same day that PM2.5 increased during wildfire season (OR, 1.057 per + 10 µg/m3; 95% CI, 1.019-1.097; P = .003) and winter inversions (OR, 1.023 per +10 µg/m3; 95% CI, 1.010-1.037; P = .0004). Risk decreased after 1 week, but during wildfire season risk rebounded at a 4-week lag (OR, 1.098 per +10 µg/m3; 95% CI, 1.033-1.167). Asthma risk for adults during wildfire season was highest in the first 3 days after PM2.5 increases, but for children, the highest risk was delayed by 3 to 4 weeks. PM2.5 exposure was weakly associated with COPD hospitalization. Ozone exposure was not associated with elevated risks. INTERPRETATION: In a large urban population, short-term increases in PM2.5 during wildfire season were associated with asthma hospitalization, and the effect sizes were greater than for PM2.5 during inversion season.

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

CFD simulation study and experimental analysis of indoor air stratification in an unventilated classroom: A case study in Spain.

Health problems and respiratory diseases are associated with poor indoor air ventilation. We investigated the air quality inside a classroom-laboratory where no ventilation is provided. The case of study, consisting of an internal enclosure, is located at the Escuela Técnica Superior de Edificación (ETSEM) of Madrid (Spain). The high height favours air stratification which is analysed in terms of temperature and CO2 spatial distribution. Temperature, air humidity, atmospheric pressure and CO2 concentration measurements were taken in time at three different height locations. A CFD numerical model was established to analyse air quality. Flow circulation is derived by solving full 3D Navier - Stokes governing equations, coupled with the thermal problem. The diffusion problem of the CO2 produced by the inner occupants is then derived from the kinematics solution. Three scenarios were taken into account: occupants seated (1), standing (2), half seated, half standing (3). Results clearly show the air stratification as a result of density gradient, which is in turn determined by temperature difference between the occupants and the surrounding air. Temperature prediction maximum relative error is contained to 3.5 %. As expected, CO2 concentration increases over time, reaching maximum values depending on the configuration considered and height location.

Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning.

This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer's calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO2, and O3 sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas.

Climate Change and Health: Public Health and Legal Strategies to Reduce Reliance on Fossil Fuels, Increase Air Quality, and Improve Human Health.

Reliance upon fossil fuels and limited greenspace contribute to poor indoor and outdoor air quality and adverse health outcomes, particularly in communities of color. This article describes justice-informed public health and legal interventions to increase access to greenspace and accelerate the transitions to renewable energy and away from gas appliances.

Ambient PM2.5 temporal variation and source apportionment in Mbarara, Uganda.

Air pollution is the leading environmental cause of death globally, and most mortality occurs in resource-limited settings such as sub-Saharan Africa. The African continent experiences some of the worst ambient air pollution in the world, yet there are relatively little African data characterizing ambient pollutant levels and source admixtures. In Uganda, ambient PM2.5 levels exceed international health standards. However, most studies focus only on urban environments and do not characterize pollutant sources. We measured daily ambient PM2.5 concentrations and sources in Mbarara, Uganda from May 2018 through February 2019 using Harvard impactors fitted with size-selective inlets. We compared our estimates to publicly available levels in Kampala, and to World Health Organization (WHO) air quality guidelines. We characterized the leading PM2.5 sources in Mbarara using x-ray fluorescence and positive matrix factorization. Daily PM2.5 concentrations were 26.7 µg m-3 and 59.4 µg m-3 in Mbarara and Kampala, respectively (p<0.001). PM2.5 concentrations exceeded WHO guidelines on 58% of days in Mbarara and 99% of days in Kampala. In Mbarara, PM2.5 was higher in the dry as compared to the rainy season (30.8 vs 21.3, p<0.001), while seasonal variation was not observed in Kampala. PM2.5 concentrations did not vary on weekdays versus weekends in either city. In Mbarara, the six main ambient PM2.5 sources identified included (in order of abundance): traffic-related, biomass and secondary aerosols, industry and metallurgy, heavy oil and fuel combustion, fine soil, and salt aerosol. Our findings confirm that air quality in southwestern Uganda is unsafe and that mitigation efforts are urgently needed. Ongoing work focused on improving air quality in the region may have the greatest impact if focused on traffic and biomass-related sources.

Motivating parents to protect their children from wildfire smoke: the impact of air quality index infographics.

Background. Wildfire smoke events are increasing in frequency and intensity due to climate change. Children are especially vulnerable to health effects even at moderate smoke levels. However, it is unclear how parents respond to Air Quality Indices (AQIs) frequently used by agencies to communicate air pollution health risks. Methods. In an experiment (3 × 2 × 2 factorial design), 2,100 parents were randomly assigned to view one of twelve adapted AQI infographics that varied by visual (table, line, gauge), index type (AQI [0-500], AQHI [1-11+]), and risk level (moderate, high). Participants were told to imagine encountering the infographic in a short-term exposure scenario. They reported worry about wildfire smoke, intentions to take risk-mitigating actions (e.g., air purifier use), and support for various exposure reduction policies. Subsequently, participants were told to imagine encountering the same infographic daily during a school week in a long-term exposure scenario and again reported worry, action intentions, and policy support. Results. Parents' responses significantly differentiated between risk levels that both pose a threat to children's health; worry and action intentions were much higher in the high-risk group than the moderate-risk group in both short-exposure (F = 748.68 p<.001; F = 411.59, p<.001) and long-exposure scenarios (F = 470.51, p<.001; F = 212.01, p<.001). However, in the short-exposure scenario, when shown the AQHI [1-11+] with either the line or gauge visuals, parents' action intentions were more similar between moderate- and high-risk level groups (3-way interaction, F = 6.03, p = .002). Conclusions. These results suggest some index formats such as the AQHI-rather than the AQI-may better attune parents to moderate levels of wildfire smoke being dangerous to children's health. Our research offers insights for agencies and officials seeking to improve current public education efforts during wildfire smoke events and speaks to the critical need to educate parents and help them act short-term and long-term to protect children's health.

The impact of education expenditure on environmental innovation.

Growing environmental challenges necessitate increased focus on sustainability education. This study examines the effects of environmental education programs in China on air and water quality perception, waste reduction, and energy consumption reduction. A comparative quantitative design with 650 participants divided into four groups was employed. Data were collected using the Environmental Sustainability Assessment Survey (ESAS) instrument to assess environmental awareness and behavior changes. Statistical tests were used to identify significant differences between groups. Findings showed significant improvements in perceived air and water quality, with web-based programs demonstrating particular success. Waste reduction efforts also varied, with web-based education again proving effective. Energy consumption reduction was most evident in the corporate sector, where leadership in electric vehicles and sustainable transportation played a key role. Supportive government policies and environmental NGOs further highlighted the power of informed environmental decision-making. This study emphasizes the critical role of environmental education in addressing sustainability challenges. It empowers individuals and communities to actively engage in environmental conservation actively, fostering a harmonious relationship between humans and the environment. Our findings have global implications, highlighting education's vital role in shaping a sustainable future.

Long-term effects of air quality on hospital readmission for heart failure in patients with acute myocardial infarction.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide, with air pollution posing significant risks to cardiovascular health. The effect of air quality on heart failure (HF) readmission in acute myocardial infarction (AMI) patients is unclear.The aim of this study was to evaluate the role of a single measure of air pollution exposure collected on the day of first hospitalization. METHODS: We retrospectively analyzed data from 12,857 acute coronary syndrome (ACS) patients (January 2015-March 2023). After multiple screenings, 4023 AMI patients were included. The air pollution data is updated by the automatic monitoring data of the national urban air quality monitoring stations in real time and synchronized to the China Environmental Monitoring Station. Cox proportional hazards regression assessed the impact of air quality indicators on admission and outcomes in 4013 AMI patients. A decision tree model identified the most susceptible groups. RESULTS: After adjusting for confounders, NO2 (HR 1.009, 95% CI 1.004-1.015, P = 0.00066) and PM10 (HR 1.006, 95% CI 1.002-1.011, P = 0.00751) increased the risk of HF readmission in ST-segment elevation myocardial infarction (STEMI) patients. No significant effect was observed in non-STEMI (NSTEMI) patients (P > 0.05). STEMI patients had a 2.8-fold higher risk of HF readmission with NO2 > 13 µg/m3 (HR 2.857, 95% CI 1.439-5.670, P = 0.00269) and a 1.65-fold higher risk with PM10 > 55 µg/m3 (HR 1.654, 95% CI 1.124-2.434, P = 0.01064). CONCLUSION: NO2 and PM10 are linked to increased HF readmission risk in STEMI patients, particularly when NO2 exceeds 13 µg/m3 and PM10 exceeds 55 µg/m3. Younger, less symptomatic male STEMI patients with fewer underlying conditions are more vulnerable to these pollutants.

Dataset of air pollutants (PM2.5, PM10, CO) concentrations in the export processing area of Dhaka, Bangladesh.

This study presents a valuable dataset on air quality in the densely populated Dhaka Export Processing Zone (DEPZ) of Bangladesh. It included a dataset of Particulate Matter (PM2.5, PM10) and CO concentrations with Air Quality Index (AQI) values. PM data was collected 24h, and CO data was collected 8h monthly from 2019 to 2023 using respirable dust sampler APS-113NL for PM2.5, APS-113BL for PM10, and LUTRON AQ9901SD Air Quality Monitor Data Logger used to measure CO concentration data. Data sampling locations are selected based on population density, and employment data for DEPZ is also included, highlighting a potential rise in population density. This article also forecasted pollutant concentrations, AQI values, and health hazards associated with air pollutants using the Auto Regressive Moving Average (ARIMA) model. The performance of the ARIMA model was also measured using root mean squared error (RMSE) and mean absolute error (MAE). However, this can be used to raise awareness among the public about the health hazards associated with air pollution and encourage them to take measures to reduce their exposure to air pollutants. In addition, this data can be instrumental for researchers and policymakers to assess air pollution risks, develop control strategies, and improve air quality in the DEPZ.

Atomically thin-layered WS2 based resistive sensors for detection of CO and NO2 at room temperature.

The number of layers present in a two-dimensional (2D) nanomaterial plays a critical role in applications that involve surface interaction, for example, gas sensing. This paper reports the synthesis of 2D WS2 nanoflakes using the facile liquid exfoliation technique. The nanoflakes were exfoliated using bath sonication (BS-WS2) and probe sonication (PS-WS2). The thickness of the BS-WS2 was found to range between 70 - 200 nm, and that of PS-WS2 varied from 0.6 to 80 nm, indicating the presence of single to few layers of WS2 when characterized using atomic force microscope (AFM). All the WS2 samples were thoroughly characterized using electron microscopes, X-ray diffractometer, Raman spectroscopy, UV-Visible spectroscopy, Fourier Transform Infrared spectroscope, and thermogravimetric analyser. Both the nanostructured samples were exposed to 2 ppm of NO2 at room temperature. Interestingly, BS-WS2 which comprises of a greater number of WS2 layers exhibited -14.2% response as against -3.4% response of PS-WS2, the atomically thin sample. The BS-WS2 sample was found to be highly selective towards NO2 but was slower (with incomplete recovery) as compared to PS-WS2. The PS-WS2 sample was observed to exhibit -11.9 to - 27.4% response to 2-10 ppm of CO and -3.4 to 35.2% response to 2-10 ppm of NO2 at room temperature, thereby exhibiting the potential to detect two gases simultaneously. These gases could be accurately predicted and quantified if the response times of the PS-WS2 sample were considered. The atomically thin WS2-based sensor exhibited a limit of detection (LOD) of 131 and 81 ppb for CO and NO2, respectively.

Air quality in a revitalized special economic zone at the center of an urban monocentric agglomeration.

In this study, we have examined the air quality within a revitalized, post-industrial urban area in Lódz, Poland. The use of Dron technology with mobile measurement equipment allowed for accurate assessment of air quality (particulate matter and gaseous pollutants) and factors influencing air quality (wind speed and direction) on a local scale in an area of 0.18 km2 and altitudes from 2 to 50 m. The results show that the revitalization carried out in the Lodz special economic zone area contributed to eliminate internal air pollution emitters through the use of ecological and effective heat sources. The exceedances permissible concentration values were local, and concerned mainly the higher measurement zones of the troposphere (more than 30 m above ground level). In the case of gaseous pollutants, higher wind speeds were associated with a decrease in the concentration of SO2 and an increase in H2S concentration. In both cases, the wind contributed to the occurrence of local areas of accumulation of these gaseous pollutants in the spaces between buildings or wooded areas.

Local weather phenomenon Galerna influences daily radon concentrations in northern Iberian Peninsula.

One of the main factors that affect urban air quality is meteorology. The objective of this study is to understand and characterise the influence that "Galerna" (GL) (an abrupt westerly change over the northern coast of Spain) has on the daily variability of the air quality over Bilbao city (northern Spain). A total of 46 one-day periods from 2009 to 2019 during which GL have been analysed. Radon observations at the Bilbao city radiological station were used because radon is a suitable atmospheric tracer by which to assess and characterise air quality dynamics. The cluster analysis of these periods revealed that increases in radon concentrations, mainly in the afternoon, are associated with the occurrence of GL, but that, this increase in the daily variability of radon concentrations in Bilbao is not reflected in all these GL periods. This variability in the impact of the GL scenario on radon concentrations is associated with the location of Bilbao: along the Nervion valley and 16 km from the coast. The analysis of three GL periods using 10-min surface meteorological and radon data showed an anomalous increase in radon with the arrival of maritime winds, which is associated with the process of a progressive accumulation of radon concentrations over the coastal area in the previous days, and the displacement of these air masses inland owing to the development of the GL event. Our results consequently identify the impact of GL on urban air quality in the afternoon, along with the fact that the complex layout of this coastal area, with the presence of valleys and mountains, favours the formation of reservoir layers above the coastal and valley areas, thus influencing on daily variability of air pollution concentrations. These increases in radon concentrations do not present a significant impact on human health.

The impact of air pollution on the facial skin of Caucasian women using real-life pollutant exposure measurements.

BACKGROUND: To date, studies examining the effect of air pollution on skin characteristics have relied on regional pollution estimates obtained from fixed monitoring sites. Hence, there remains a need to characterize the impact of air pollution in vivo in real-time conditions. We conducted an initial investigation under real-life conditions, with the purpose of characterizing the in vivo impact of various pollutants on the facial skin condition of women living in Paris over a 6-month period. MATERIALS AND METHODS: A smartphone application linked to the Breezometer platform was used to collect participants' individual exposures to pollutants through the recovery of global positioning system (GPS) data over a 6-month period. Daily exposure to fine particulate matter (PM 2.5 µm and PM 10 µm), pollen, and air quality was measured. Facial skin color, roughness, pore, hydration, elasticity, and wrinkle measurements were taken at the end of the 6-month period. Participants' cumulated pollutant exposure over 6 months was calculated. Data were stratified into two groups (lower vs. higher pollutant exposure) for each pollutant. RESULTS: 156 women (20-60 years-old) were recruited, with 124 women completing the study. Higher PM 2.5 µm exposure was associated with altered skin color and increased roughness under the eye. Higher PM 10 µm exposure with increased wrinkles and roughness under the eye, increased pore appearance, and decreased skin hydration. Exposure to poorer air quality was linked with increased forehead wrinkles and decreased skin elasticity, while higher pollen exposure increased skin roughness and crow's feet. CONCLUSION: This study suggests a potential correlation between air pollution and facial skin in real-life conditions. Prolonged exposure to PM, gases, and pollen may be linked to clinical signs of skin ageing. This study highlights the importance of longer monitoring over time in real conditions to characterize the effect of pollution on the skin.

A scalable, passive sampling method for the quantification of airborne allergens.

Traditionally, airborne concentrations of aeroallergens are sampled in a single location by an active sampler, which requires electricity and regular maintenance. However, aeroallergen concentrations may vary widely over urban and rural environments, requiring a method that is cost-effective and scalable so that many measurements can be made across an air shed. We developed such a method that uses passive sampling and light microscopy for analysis. Inexpensive and easy to operate, passive samplers rely on the gravitational settling of particles onto microscope slides. This determines airborne pollen concentration through: 1) sample collection using a modified Durham sampler, 2) preparation of samples for microscopy and strategic sample imaging, and 3) simplified particle measurements and calculation of pollen concentration following deposition velocity models proposed by Scheppegrell [1] and Wagner and Leith [2]. This method was verified with two sampling campaigns during the ragweed season of 2020 and the tree pollen season of 2021. The concentrations determined with the passive and Burkard sampling methods were found to be well-correlated (r > 0.99, r = 0.87) and precise (%CV = 20 %, 21 %). The validation of passive samplers will enable measurements of aeroallergens over wider spatial scales and help determine where aeroallergen exposure risks are greatest. •An inexpensive and low-cost method was developed to determine airborne pollen counts.•The method was evaluated for its accuracy and reproducibility.•The method can be applied to examine the concentrations and spatial variability of airborne pollen.

Temporal change and impact on air quality of an energy recovery plant using the M-BACI design in Gipuzkoa.

A significant concern in our society is the potential impact on both health and the environment of air pollutants released during the incineration of waste therefore, it is crucial to conduct thorough control and monitoring measures. In this context, the objectives of this research was to study the evolution of particulate matter (PM2.5) and associated trace elements during the period before and after the installation of an Energy Recovery Plant (ERP). For that, a descriptive and temporal analysis of PM2.5 concentration and composition were performed on two similar areas (impact / control) using the Before-After/Control-Impact (BACI) design and two periods (before from 01/01/2018 to 06/02/2020 and after from 10/12/2020 to 30/09/2022). Results showed a decrease in the levels of PM2.5 and associated trace elements is observed in the impact zone (IZ) and in the control zone (CZ) throughout the study period. In the case of PM2.5, the most notable decrease occurred in the period of the start-up of the ERP, a period that coincides with the confinement and restrictions of COVID, with a subsequent increase in both zones, without reaching the levels observed in the period prior to the start-up of the ERP. Selenium is the only trace element that increases significantly in the IZ. In conclusion, a decrease is observed for all pollutants except selenium in both zones, although less pronounced in the IZ. Since selenium already showed an upward trend in the phase prior to the start of the ERP, it is necessary to investigate its evolution and find out the possible cause.

Frailty was associated with atmospheric NO2 levels: a geospatial approach.

BACKGROUND: Recent evidence has linked air pollution with frailty, yet little is known about the role of NO2 in this association. Our aim was to assess the association between frailty and NO2 air concentrations in Mexican older adults. METHODS: We used georeferenced data from the population-based Nutrition and Health Survey in Mexico (NHNS) 2021, representative of national and subnational regions, to measure a frailty index based on 31 health deficits in adults aged 50 and over. Air pollution due to NO2 concentrations was estimated from satellite images validated with data from surface-level stations. Maps were produced using Jensen's Natural break method. The association of frailty and NO2 concentrations was measured using the frailty index (multivariate fractional response logit regression) and a frailty binary variable (FI ≥ 0.36, multivariate logit regression). RESULTS: There was a positive and significant association of the frailty index with the NO2 concentrations, adjusting for age, sex, urban/ rural area, years of education, socio-economic status, living arrangement, particulate matter smaller than 2.5 microns, and indoor pollution. For each standard deviation increase of NO2 concentrations measured ten years before the survey, the odds of being frail were 15% higher, and the frailty index was 14.5% higher. The fraction of frailty attributable to NO2 exposure ranged from 1.8 to 23.5% according to different scenarios. CONCLUSION: Frailty was positively associated with exposure to NO2 concentrations. Mapping frailty and its associated factors like NO2 air concentrations can contribute to the design of targeted pro-healthy aging policies.