Air Quality Index Research Articles

Application of a methodology for the development of air quality and health indices in Latin American countries

Application of a methodology for the development of air quality and health indices in Latin American countries

The association between the Daily Air Quality Index, inhaler prescriptions and mortality following Guy Fawkes Night celebrations in the UK

The association between the Daily Air Quality Index, inhaler prescriptions and mortality following Guy Fawkes Night celebrations in the UK

Comparing mechanical ventilation control strategies for indoor air quality: Monitoring and simulation results of a school building in northern Italy

Demand-controlled ventilation systems and carbon dioxide monitoring are critical to ensure indoor air quality (IAQ) comfort conditions. Their importance has become significantly more evident since the COVID-19 pandemic crisis. Control strategies for mechanical ventilation based on CO2 concentration are commonly used, although they are rarely applied in Italian and other Southern European country schools. Moreover, optimised ventilation systems can help maintain indoor air quality while reducing energy consumption for heating (i.e. exploiting heat recovery). The proposed paper aims to compare different remote-control strategies to manage three detached mechanical ventilation units (DMV) installed in a junior-high-school demo building in Torre Pellice (Northwest Italy), monitored with room detail since April 2021. Eight IAQ control logics are analysed considering both in situ tests, handling DMV accordingly to room sensors, and simulation tests using the Energy Management System of EnergyPlus in the neutral season. All different solutions are compared by considering air quality, temperature, and energy need indicators. Additionally, a yearly energy verification via hourly simplified analyses is performed by considering fan consumption and energy conservation. Threshold-based control logics result to be more effective than time-dependent ones, while all cases guarantee very high IAQ levels, although energy savings are limited. The choice of the correct control threshold(s) is site-dependent being influenced by user local habits.

Lichen diversity, substrate preference and environmental dynamics as indicators of air quality

Lichen diversity, substrate preference and environmental dynamics as indicators of air quality

A multi-task stations cooperative air quality prediction system for sustainable development

In recent years, A series of environmental problems caused by air pollution have attracted widespread attention. Air quality forecasting has become an indispensable part of people’s daily life. However, the traditional air quality prediction (AQP) model WRF-CMAQ simulation system faces several challenges: (1) the fuzziness of pollutant formation mechanism; (2) the hardness of integrating the features of meteorological conditions; (3) the difficulty of cooperating among monitoring stations. To deal with these challenges, we propose a multi-task station cooperative air quality prediction (MTSC-AQP) system for sustainable development. The MTSC-AQP system contains three modules: air quality analysis, a WRF-LSTM module for initial pollutant prediction, and multi-station cooperative AQP. Through air quality analysis, it can calculate the air quality index (AQI) and analyze the correlation between pollutants and meteorological conditions. Then, the WRF-LSTM module integrates spatio-temporal multi-source data to forecast the initial concentration of pollutants. Finally, the system incorporates the gravity model to predict the final AQI. Extensive experiments conducted on real-world datasets show the effectiveness of forecasting the AQI by using the MTSC-AQP system.

A novel hybrid optimization model for evaluating and forecasting air quality grades.

Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models.

Gaseous air quality and health risk assessment of high school kitchens in the Kumasi Metropolis

Cooking is a major factor that affects the quality of indoor air especially in kitchens. It is a daily activity essential for supplying the requisite energy and nutrients for living through food consumption. Exposure to air pollutants from cooking with solid fuels in domestic and commercial kitchens is a leading health risk in developing nations like Ghana. Real-time monitoring of gaseous pollutants (CO, SO2, NO2) was done during eight hours of occupational cooking in different cookstove kitchens of 14 Senior High Schools in the Kumasi Metropolis, Ghana. The traditional and improved cookstove kitchens were fueled by firewood whereas the briquette cookstove kitchens were fueled by palm kernel shell briquette. The observed median concentrations in the kitchens were 0.43–39.44 mg m−3 for CO, 0.07–0.36 mg m−3 for NO2, and 0.19–0.61 mg m−3 for SO2. The concentrations in the traditional and briquette cookstove kitchens exceeded the respective World Health Organization (WHO) thresholds of 4 mg m−3 for CO, 0.025 mg m−3 for NO2, and 0.04 mg m−3 for SO2 mg m−3. The air quality index of traditional cookstove kitchens was classified as hazardous for human occupancy. Hazard indices > 1 revealed the likelihood of significant non-carcinogenic health risks for cooks’ occupational exposure to gaseous pollutants in all the cookstove kitchens. This study is essential as it informs on gaseous air quality during large-scale cooking within institutional kitchens with different cookstove types in a developing country. The study fills a gap in literature by providing real-time concentrations of gaseous air pollutants to which cooks in high school kitchens of a developing country are occupationally exposed. The study recommends the urgent transition of large-scale kitchens in developing countries to cleaner energies and cookstove types, alongside regular monitoring and enforcement of air quality guidelines, to safeguard occupational health.

Determining Air Quality Influential Parameters Using Machine Learning Techniques

Air quality is an important issue in public health and the environment. This research aims to develop an air quality prediction model based on PM10 and PM2.5 parameters using various regression and machine learning approaches. The dataset used includes air pollutant standard index (ISPU) data from a number of stations in the Jakarta area with an observation period from January to April 2024. The research method includes collecting datasets, reviewing literature and testing several models of machine learning techniques. Furthermore, the handling of outliers was carried out using the numeric outliers node and data normalization to prepare the data before dividing the training and testing data. The models evaluated include Linear Regression, Random Forest Regression, Gradient Boosted Trees, and Multilayer Perceptron (MLP), with validation using 10 times cross-validation. The results showed that the Random Forest Regression and Gradient Boosted Trees models provided good prediction performance for both PM10 and PM2.5 parameters. Random Forest Regression showed the lowest RMSE value on testing data for PM10 (0.048) and PM2.5 (0.037), while Gradient Boosted Trees showed the lowest RMSE value on training data for PM2.5 (0.032). The process of handling outliers and normalizing the data successfully improved the prediction accuracy of the model. Suggestions for future research include the exploration of new models, the addition of meteorological and socio-economic variables, and the application of models in real-time air quality monitoring systems.

Using syndromic surveillance to rapidly assess the impact of a June 2023 wildfire smoke event on respiratory-related emergency department visits, Massachusetts, United States

Abstract A statewide air quality advisory was issued in Massachusetts for 6–7 June 2023 due to smoke originating from wildfires in Canada. Of particular concern was fine particulate matter, which has an aerodynamic diameter of ⩽2.5 μm (PM2.5) and has been linked to adverse respiratory outcomes. The objective of this study was to rapidly assess the impact of this wildfire smoke event on respiratory-related emergency department (ED) visits among Massachusetts residents. For exposure, daily air quality index (AQI) data from the US Environmental Protection Agency were used. Massachusetts counties, where for each day from 6 to 8 June 2023, the daily AQI was ⩾101 (i.e. unhealthy air quality), were considered exposed. For each exposed period, two unexposed reference periods where AQI < 101 (i.e. ‘good’ or ‘moderate’ air quality) were identified within the two weeks prior to the exposed period, with the same days of the week and in the same county. Data from the Massachusetts Department of Public Health’s syndromic surveillance system were used to examine daily counts of ED visits for asthma, air-quality-related respiratory illness, and all causes by county of residence, age group, race, and Hispanic/Latino ethnicity. For each outcome, the numbers of ED visits were compared between the exposed and reference periods. Overall, there were no large increases in ED visits for any conditions examined during this wildfire smoke event. However, residents who were aged 18–64 years, Hispanic/Latino or White experienced small but not statistically significant increases in asthma-related ED visits. These potential differences in the effect on asthma-related ED visits by age and race/ethnicity may be relevant for analyses of future events. This study provides an example of how real-time, publicly available exposure data can be used in conjunction with outcome data from syndromic surveillance to rapidly examine the impact of wildfires and other acute environmental events on health.

Optimized air quality management based on air quality index prediction and air pollutants identification in representative cities in China

With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi’an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi’an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Air Quality Prediction and Ranking Assessment Based on Bootstrap-XGBoost Algorithm and Ordinal Classification Models

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper.

Unraveling the nexus: exploring the interplay of mass media, personality traits and social norms in fostering protective behavior against smog

Purpose The rise in industrialization, economic development and urbanization has altered the composition of atmospheric air. Air pollutants have significantly impacted Pakistan's urban areas, resulting in the lowest air quality index readings. To understand disinclined behavior such as using air purifiers and wearing masks, this study aims to explore the relationship between individual personality traits and mass media effects. Design/methodology/approach This study used a cross-sectional survey with a model based on previous theories. Data from 2,000 adults were self-administered and analyzed using partial least squares structural equation modeling (PLS-SEM). Findings PLS-SEM confirmed six hypotheses, revealing mass media's direct and mediating roles in promoting critical psychological perceptions for protective behavior. The empirical results indicate that neuroticism, agreeableness and openness traits reinforce adherence behavior by intensifying threat perception. Practical implications This paper highlights significant implications for media campaign planners and provides more precise and comprehensive theoretical guidance. In the context of environmental challenges like smog, the findings illustrate how an understanding of personality traits can improve the efficacy of public health interventions through adherence to behaviors like mask-wearing and air purifier use. Addressing individual psychological characteristics when creating mass media campaigns to encourage public adherence to health protection measures related to environmental hazards. Originality/value Despite health campaigns and awareness, public compliance with measures like mask-wearing and air purifier use remains minimal. This study explores untapped mass media effects based on individual personality traits to understand this behavior.

“Occupational exposure to charcoal smoke and dust, a major risk factor for COPD: a multiregional cross-sectional study in the Democratic Republic of Congo”

Occupational exposure to charcoal smoke and dust is a threat to workers respiratory system. What is the prevalence of chronic obstructive pulmonary disease (COPD) in charcoal workers as compared to farmers in rural areas of Democratic Republic of Congo (DRC)? This cross-sectional, comparative, and multi-site study was performed in the charcoal-producing provinces of South-Western DRC. We randomly included charcoal workers and compared them to farmers (18 to 70 years old). Air quality indexes, anthropometric, physical activity, sociodemographic characteristics, and related medical events data were recorded. A Lung function questionnaire was used to assess respiratory symptoms (RS) and spirometry was performed. COPD was defined as the presence of RS for more than 3 months with a FEV1/FVC ratio below the lower limit of normal. The prevalence of COPD was calculated, and logistic regression was used to identify COPD-associated factors. We included 485 subjects between August 2020 and July 2021. Charcoal Producers (CP, n = 229) were compared with farmers (n = 118), and charcoal saleswomen (CS, n = 72) were compared to vegetable saleswomen (VS, n = 66). Respective groups were similar in age, job seniority, height, and weight. The air was more polluted at charcoal workplaces. The prevalence of COPD was higher in CP than in farmers (39.7% vs 14.4%; P < 0.0001), and in CS compared to VS (40.3% vs 13.6%; P < 0.0001). Being a charcoal worker was independently associated with COPD in the CP and farmers group: adjusted OR, 3.54 (95% CI, 1.94-6.46), and, in the saleswomen group: 7.85 (95% CI, 2.85-21.5), where it was also independently associated with young age: 0.85 (0.80-0.93) and monthly income: 0.88 (0.83-0.96). In rural areas of DRC, producing or selling charcoal is associated with a higher risk of COPD.

Impact of fine particulate pollution exposures on respiratory health in a megacity of Pakistan

Air pollution poses a substantial barrier to global environmental sustainability and citizen well-being. However, there is a lack of research that specifically examines the effects of short-term exposure to PM2.5 and its components on health outcomes in developing nations in Asia, regions often cited as having some of the most severe urban air quality issues globally. The present study evaluated the associations between PM2.5 components and hospital admissions (HA) and emergency room visits (ERV) for respiratory diseases in the megacity of Karachi, Pakistan. We measured PM2.5 constituents, including black carbon (BC) and ionic species (sulfate - SO42−; nitrate - NO3−; and ammonium - NH4+) at two sites (August 2008 to August 2009) in Karachi. The HA and ERV, respiratory disease outcome data, were collected from three local, large tertiary care hospitals. We assessed the lag structure of excess risk (ER) of the pollutants-outcomes association (0–6 single and cumulative lag days) using time-series quasi-Poisson models after adjusting for temperature, humidity, and day of the week. Among a total 13,827 patients, the highest ERs for all respiratory urgent care use (UCU) were observed for PM2.5 (10.3, 95% CI: 2.59%-18.59), NH4+ (9.58%, 95% CI: 1.50%–18.30%), air quality index (9.11%, 95%CI: 2.54%–16.09%) and SO42− (7.26%, 95% CI: 1.03%–13.87%) within 0–4 lag days. Additionally, patients with chronic obstructive pulmonary disease (COPD), tuberculosis (TB), or other pulmonary diseases and older or male patients were more vulnerable to these pollutants. This first study in Pakistan found that PM2.5 and its constituents were associated with respiratory HAs and ERVs for the inhabitants of the megacity of Karachi. These associations varied by different PM constituents, disease subtypes, age, and gender. Our results provide important information to policymakers for developing regulations for improving air quality and public health. Further studies are urgently needed in other developing countries to disentangle the air pollution health effects.

Assessment of Air Quality Index of Kolkata, Howrah, and 24 Parganas North during COVID- 19 Lockdown

In the course of COVID- 19 pandemic, a nationwide lockdown in India has been imposed from 22nd March 2020 extended to June end, thereafter a partial lockdown was enforced as a precautionary measure to confine the spread of COVID- 19. The anthropogenic activity was restricted during lockdown therefore the pollution level across many cities in India reduced drastically. This eventually led us to analyze the air quality scenario of Kolkata metropolitan city along with suburban areas of North 24 Parganas and Howrah in the state of West Bengal, India. In the present research, the air quality parameters like PM10, PM2.5, SO2, NO2, CO, O3, and NH3 were analyzed the during lockdown phase of 23rd March 2020 to 30th June 2020. A comparative study was done with the air quality data of the pre-lock down phase (March 2019 to 4th March 2020) and an Air Quality Index (AQI) was prepared. During lockdown coal/ fuel consumption was low due to the restriction of vehicle movement and road transport thus carbon emission in the air was less. AQI improved enormously with reduction of SO2 content to &lt;80 %; among other pollutants NO2 and NH3 reduced to &lt;54 % and &lt;91 % in Kolkata, Howrah, and 24 Parganas North. P.M 2.5 drastically reduced in the regions of Kolkata, Howrah, and 24 Parganas North of West Bengal effectively to &lt;81%, &lt;91 %, and &lt;95% respectively. P.M 10 reduced to &lt;75%, &lt;79%, and &lt; 81 % in Kolkata, Howrah, and 24 Parganas North. In Kolkata there was 55% reduction in the level of CO. There was 4 folds reduction of P.M2.5 and P.M10 in Kolkata compared to March- June 2019. About 50 % to 60 % improvement in air quality was identified within 7days of the lockdown. Improvement of air quality during lockdown has benefited the environment and ecology.

Analysis of carbon and nitrogen from atmospheric sources by bulk deposition sampling at various locations in Germany

Atmospheric deposition of particulate matter is an important indicator of air pollution and a significant factor in material surface fouling. The elemental composition of this nutrient-containing dust depends largely on the exposure region and time as well as on climate. Therefore, in this paper we report an analysis of atmospheric pollutions with a self-made low-cost bulk deposition sampler directed at sampling deposition via air transport and rainfall. We used the device in diverse environments - thus comparing an urban region, an area surrounded by forest and an area mainly dominated by agriculture. The total organic carbon (TOC) and total nitrogen (TN) amounts were selected as indicator parameters and analyzed in a biweekly rhythm for three and a half and two years, respectively. The TOC value responded to particulate matter in the urban area, especially significant were the influences of the New Year's firework in urban and pollen in the rural forest area. In contrast, the TN value was more under the influence of the nitrogen emissions in the agriculture-dominated area. However, the TN value did not correlate with the NOx values in the urban area because the atmospheric nitrogen emissions in the city might originate from various emission sources. Summarizing, the TOC and TN values of the self-made low-cost bulk deposition sampler were in good agreement with environmental events of their immediate surrounding. Moreover, the selected containers and sampling procedures are universally applicable to monitor and analyze organic as well as inorganic parameters (e.g. metal ions) of atmospheric deposition.

A comparative study of indoor and outdoor air quality and associated potential health effects in Gurugram, India.

Abstract Deteriorating air quality remains a leading reason for global human illness and death. This study aims to investigate and compare inside and outside ambient air quality in 15 locations of Gurugram, India. Research technique adopted comprised of extracting outdoor air quality parameter values via Air Quality Index reporting stations for the entire year 2022. Parallel to that, indoor air quality parameter values of houses close to those monitoring stations were recorded through low-cost IoT based AirCubic sensors (T1595) as PM2.5, PM10, relative humidity (RH) and carbon monoxide (CO). After statistical evaluation, it was found that there were significant variations in outdoor and indoor air quality with maximum variations seen in colder season. The study revealed that PM levels were generally lower inside homes than outsides, while CO and RH concentrations were higher indoors. However, these levels often exceeded the acceptable thresholds set by both Indian national and global air quality standards, that could potentially result in harmful health effects, as well as increased risk of short and long-term illnesses, and even death. It may be concluded that more research, extensive air quality monitoring, data sharing, citizen awareness, and better technical and policy inputs are required to maintain healthy air quality for a sustainable lifestyle.

The impact of urbanization on environmental degradation in Jakarta

Background: The urbanization discussed in this paper refers to the process of expansion in the proportion of the population living in urban areas. Urbanization is seen as one of the processes of increasing population that drives the functional change of an area, from rural to urban activities. The case discussed in this paper is urbanization occurring in Jakarta city. This paper aims to examine the relationship between urbanization and the decline in environmental quality, especially in Jakarta city. Method: In assessing environmental quality, the reference used follows that set by the Ministry of Environment and Forestry, namely the Environmental Quality Index (EQI) which consists of three elements, namely the Water Quality Index (WQI), Air Quality Index (AQI) and Land Cover Quality Index (LCQI), so the variables used are WQI, AQI and LCQI. The data collection method used in this paper is secondary data collection through literature review and official data sources from related stakeholders. The collected data is then analyzed descriptively to determine the relationship between urbanization and each of these variables. Results: Based on the literature review and the results of the LCQI data used, it appears that there is a negative correlation between population growth and land cover in Jakarta Province. This condition is marked by an increase in floods and also an increase in surface temperature. In terms of air quality, this paper found a negative correlation between urbanization and AQI. However, for the condition in 2013 - 2018, a positive correlation was found between the population and AQI. Conclusion: This indicates that an increase in population is not always followed by a decrease in environmental quality. Meanwhile, in relation to water quality, population growth from the urbanization process has a significant impact on water quality in Jakarta. Novelty/Originality of this study: The development of a sophisticated and comprehensive correlation analysis method between population growth and environmental quality indicators (IKLH, IKA, IKU), which integrates spatial and temporal data, provides a predictive tool that city governments can use to monitor, manage, and plan mitigation strategies for the impacts of urbanization on the environment more effectively and proactively, and supports data-based decision-making in sustainable urban planning.

Associations of weather and air pollution with objective physical activity and sedentary time before and after bariatric surgery: a secondary analysis of a prospective cohort study

Purpose. Identifying factors that influence moderate-to-vigorous intensity physical activity (MVPA) and sedentary time in metabolic and bariatric surgery (MBS) patients is necessary to inform the development of interventions. Weather/environmental factors may be especially important considering rapid climate change and the vulnerability of people with obesity to heat and pollution. Our study aimed to examine the associations of weather (maximal, average and Wet Bulb Globe Temperatures), and air pollution indices (air quality index [AQI]) with daily physical activity (PA) of both light (LPA) and MVPA and sedentary time before and after MBS. Materials and methods. Participants (n = 77) wore an accelerometer at pre- and 3, 6, and 12-months post-MBS to assess LPA/MVPA/ sedentary time (min/d). These data were combined with participants’ local (Boston, MA or Providence, RI, USA) daily weather and AQI data (extracted from federal weather and environmental websites). Results. Multilevel generalized additive models showed inverted U-shaped associations between weather indices and MVPA, with a marked reduction in MVPA for daily maximal temperatures ≥20 °C. Sensitivity analysis showed a less marked decrease of MVPA (min/d) during higher temperatures after versus before MBS. Both MVPA before and after MBS and sedentary time before MBS were negatively impacted by higher AQI levels. Conclusion. This study is the first to show that weather and air pollution indices, even in locations with good AQI and moderate temperatures, are related to variability in activity behaviors, particularly MVPA, during pre- and post-MBS. Weather/environmental conditions should be considered in MVPA prescription/strategies for adults who have undergone MBS.