High Levels Of Air Pollution Research Articles

How Do Neighbourhood and Working Environment Affect Green Commuting in China? A Resident Health Perspective

Commuting contributes to high levels of greenhouse gases and air pollution. The recently advocated ‘green commuting’, i.e. active and public modes of transport, will be conducive to low-carbon and environmentally friendly transport. A baseline goal of urban planning is to promote health; however, few studies have explored the health-related impacts of environments at both ends of the commute on residents’ commuting mode choices. To fill the gap, this study proposes to consider the impact of the neighbourhood and working environment on green commuting from a health perspective. Using a sample of 15,886 people from 368 communities in China, three generalised multilevel linear regression models were estimated. Physical and psychological health were combined to further analyse health-related environmental attributes on the commuting choices of residents with different health levels. The results indicate that the working environment exerts more substantial effects on ‘green commuting’ than the neighbourhood environment, especially for workplace satisfaction. Moreover, we found that a good working environment and relationships will significantly encourage the sub-healthy group to choose active commuting. These findings are beneficial for policymakers to consider focusing on reconciling neighbourhood and working environments and meeting the commuting requirements of the less healthy group.

Particulate Pollution from New Year Fireworks in Honolulu

Fireworks have long been a problem in Hawaii despite restrictions on their use. They cause accidents and high levels of noise and air pollution and are accompanied by admissions to hospitals from accidents and breathing difficulties. There was some initial success in reducing injuries and particulate concentrations after the implementation of the permit requirements from New Year of 2012, but the recent years of 2022 and 2023 have witnessed elevated air pollution at New Year. It seems unlikely that this arises from weather conditions as a random forest analysis suggests that the effects of local climate are quite small, and particulate pollution at New Year arises from fireworks. The pollution does not seem related to the purchase of the permitted 5000 firecrackers. Locally, there is suspicion that injury and pollution more likely arises from illegally purchased aerial fireworks. Regulatory authorities have found it difficult to estimate the quantity of illegal fireworks, and preventing their import has proved a difficult task. Such problems mean that it is important to enhance our understanding of local environmental problems caused by fireworks and improve compliance with the regulations, without diminishing enjoyment of the New Year celebration.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020–October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020–2022) period air quality recorded low improvements relative to (2015–2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021–2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022–1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Characterization of PCDD/F and dl-PCB levels in air in Gipuzkoa (Basque Country, Spain)

This research examines the levels and trends of pollutants, specifically 17 congeners of PCDD/Fs and 12 dl-PCBs, in the air measured in the province of Gipuzkoa (Basque Country, Spain). The study used PCDD/Fs, dl-PCB, and the sum of dioxin-like compounds as separate response variables. A total of 113 air samples were collected and analyzed using the method described in the European Standard (EN-1948:2006) from two industrial areas. The results were analyzed using non-parametric test to assess the variability of these pollutants based on different factors (year, season and day of the week) and General Linear Models to assess the weight of each factor. The study found that the toxic equivalents (TEQs) for PCDD/Fs were 12.29 fg TEQm−3 and for dl-PCBs were 1.63 fg TEQm−3, which were in a similar range or lower than those observed in other national and international studies in industrial areas. The results showed temporal variations, with higher levels of PCDD/Fs in autumn-winter than in spring-summer and higher levels of PCDD/Fs and dl-PCBs during weekdays than on weekends. The industrial area where the energy recovery plant (ERP) will be located had higher levels of air pollutants due to the presence of two PCDD/Fs emitting industries nearby, as indicated by the Spanish Registry of Polluting Emission Sources. Both industrial areas showed similar profiles of PCDD/Fs and dl-PCBs, with the PCDD/F profiles dominated by OCDD, 1,2,3,4,6,7,8-HpCDD, and 1,2,3,4,6,7,8-HpCDF in terms of concentrations and 1,2,3,7,8-PeCDD, 2,3,4,7,8-PeCDF, and 2,3,7,8-TCDD in terms of TEQs. The dl-PCB profiles were dominated by PCB 118, PCB 105, and PCB 77 in terms of concentrations and PCB 126 in terms of TEQs. The findings of this study can serve as an indicator of the potential impact of ERP on the health of the resident population and the environment.

No Excess of Mortality from Lung Cancer during the COVID-19 Pandemic in an Area at Environmental Risk: Results of an Explorative Analysis

The COVID-19 pandemic and the restrictive measures associated with it placed enormous pressure on health facilities and may have caused delays in the treatment of other diseases, leading to increases in mortality compared to the expected rates. Areas with high levels of air pollution already have a high risk of death from cancer, so we aimed to evaluate the possible indirect effects of the pandemic on mortality from lung cancer compared to the pre-pandemic period in the province of Taranto, a polluted site of national interest for environmental risk in the south of Italy. We carried out a retrospective observational study on lung cancer data (ICD-10: C34) from the Registry of Mortality (ReMo) for municipalities in Taranto Province over the period of 1 January 2011 to 31 December 2021. Seasonal exponential smoothing, Holt-Winters additive, Holt-Winters multiplicative, and auto-regressive integrated moving average (ARIMA) models were used to forecast the number of deaths during the pandemic period. Data were standardized by sex and age via an indirect method and shown as monthly mortality rates (MRs), standardized mortality ratios (SMRs), and adjusted mortality rates (AMRs). In Taranto Province, 3108 deaths from lung cancer were recorded between 2011 and 2021. In the province of Taranto, almost all of the adjusted monthly mortality rates during the pandemic were within the confidence interval of the predicted rates, with the exception of significant excesses in March (+1.82, 95% CI 0.11-3.08) and August 2020 (+2.09, 95% CI 0.20-3.44). In the municipality of Taranto, the only significant excess rate was in August 2020 (+3.51, 95% CI 0.33-6.69). However, in total, in 2020 and 2021, the excess deaths from lung cancer were not significant both for the province of Taranto (+30 (95% CI -77; +106) for 2020 and +28 (95% CI -130; +133) for 2021) and for the municipality of Taranto alone (+14 (95% CI -47; +74) for 2020 and -2 (95% CI -86; +76) for 2021). This study shows that there was no excess mortality from lung cancer as a result of the COVID-19 pandemic in the province of Taranto. The strategies applied by the local oncological services during the pandemic were probably effective in minimizing the possible interruption of cancer treatment. Strategies for accessing care in future health emergencies should take into account the results of continuous monitoring of disease trends.

Effect modification of the association between fine particulate air pollution during a wildfire event and respiratory health by area-level measures of socio-economic status, race/ethnicity, and smoking prevalence

Fine particulate air pollution (PM2.5) is decreasing in most areas of the United States, except for areas most affected by wildfires, where increasing trends in PM2.5 can be attributed to wildfire smoke. The frequency and duration of large wildfires and the length of the wildfire season have all increased in recent decades, partially due to climate change, and wildfire risk is projected to increase further in many regions including the western United States. Increasingly, empirical evidence suggests differential health effects from air pollution by class and race; however, few studies have investigated such differential health impacts from air pollution during a wildfire event. We investigated differential risk of respiratory health impacts during the 2008 northern California wildfires by a comprehensive list of socio-economic status (SES), race/ethnicity, and smoking prevalence variables. Regardless of SES level across nine measures of SES, we found significant associations between PM2.5 and asthma hospitalizations and emergency department (ED) visits during these wildfires. Differential respiratory health risk was found by SES for ED visits for chronic obstructive pulmonary disease where the highest risks were in ZIP codes with the lowest SES levels. Findings for differential effects by race/ethnicity were less consistent across health outcomes. We found that ZIP codes with higher prevalence of smokers had greater risk of ED visits for asthma and pneumonia. Our study suggests that public health efforts to decrease exposures to high levels of air pollution during wildfires should focus on lower SES communities.

Análisis de la relación entre la contaminación del aire ambiente y la gravedad de las descompensaciones por insuficiencia cardiaca en dos metrópolis españolas (Barcelona y Madrid)

ObjectivesTo analyze whether the high levels of air pollutants are related to a greater severity of decompensated heart failure (HF). MethodPatients diagnosed with decompensated HF in the emergency department of 4 hospitals in Barcelona and 3 in Madrid were included. Clinical data (age, sex, comorbidities, baseline functional status), atmospheric (temperature, atmospheric pressure) and pollutant data (SO2, NO2, CO, O3, PM10, PM2.5) were collected in the city on the day of emergency care. The severity of decompensation was estimated using 7-day mortality (primary indicator) and the need for hospitalization, in-hospital mortality, and prolonged hospitalization (secondary indicators). The association adjusted for clinical, atmospheric and city data between pollutant concentration and severity was investigated using linear regression (linearity assumption) and restricted cubic spline curves (no linearity assumption). ResultsA total of 5292 decompensations were included, with a median age of 83 years (IQR=76-88) and 56% women. The medians (IQR) of the daily pollutant averages were: SO2=2.5μg/m3 (1.4-7.0), NO2=43μg/m3 (34-57), CO=0.48mg/m3 (0.35-0.63), O3=35μg/m3 (25-48), PM10=22μg/m3 (15-31) and PM2.5=12μg/m3 (8-17). Mortality at 7 days was 3.9%, and hospitalization, in-hospital mortality, and prolonged hospitalization were 78.9, 6.9, and 47.5%, respectively. SO2 was the only pollutant that showed a linear association with the severity of decompensation, since each unit of increase implied an OR for the need for hospitalization of 1.04 (95% CI 1.01-1.08). The restricted cubic spline curves study also did not show clear associations between pollutants and severity, except for SO2 and hospitalization, with OR of 1.55 (95% CI 1.01-2.36) and 2.71 (95% CI 1.13-6.49) for concentrations of 15 and 24μg/m3, respectively, in relation to a reference concentration of 5μg/m3. ConclusionExposure to ambient air pollutants, in a medium to low concentration range, is generally not related to the severity of HF decompensations, and only SO2 may be associated with an increased need for hospitalization.

Evidence for an association of prenatal exposure to particulate matter with clinical severity of Autism Spectrum Disorder

Early-life exposure to air pollutants, including ozone (O3), particulate matter (PM2.5 or PM10, depending on diameter of particles), nitrogen dioxide (NO2) and sulfur dioxide (SO2) has been suggested to contribute to the etiology of Autism Spectrum Disorder (ASD). In this study, we used air quality monitoring data to examine whether mothers of children with ASD were exposed to high levels of air pollutants during critical periods of pregnancy, and if higher exposure levels may lead to a higher clinical severity in their offspring. We used public data from the Portuguese Environment Agency to estimate exposure to these pollutants during the first, second and third trimesters of pregnancy, full pregnancy and first year of life of the child, for 217 subjects with ASD born between 2003 and 2016. These subjects were stratified in two subgroups according to clinical severity, as defined by the Autism Diagnostic Observational Schedule (ADOS). For all time periods, the average levels of PM2.5, PM10 and NO2 to which the subjects were exposed were within the admissible levels defined by the European Union. However, a fraction of these subjects showed exposure to levels of PM2.5 and PM10 above the admissible threshold. A higher clinical severity was associated with higher exposure to PM2.5 (p = 0.001), NO2 (p = 0.011) and PM10 (p = 0.041) during the first trimester of pregnancy, when compared with milder clinical severity. After logistic regression, associations with higher clinical severity were identified for PM2.5 exposure during the first trimester (p = 0.002; OR = 1.14, 95%CI: 1.05–1.23) and full pregnancy (p = 0.04; OR = 1.07, 95%CI: 1.00–1.15) and for PM10 (p = 0.02; OR = 1.07, 95%CI: 1.01–1.14) exposure during the third trimester. Exposure to PM is known to elicit neuropathological mechanisms associated with ASD, including neuroinflammation, mitochondrial disruptions, oxidative stress and epigenetic changes. These results offer new insights on the impact of early-life exposure to PM in ASD clinical severity.

Expansion of Roadside Air Pollution Monitoring Network in Seoul using Learning-based Optimization Method

Objectives : In this study, a learning-based optimization method is proposed and implemented for determining new monitoring sites when expanding the roadside air pollution monitoring network. Utilizing the bigdata available in Seoul, this decision-making tool is developed that takes into account the objectives of selecting new monitoring sites and incorporates social, economic, and environmental characteristics. The optimized results can suggest potential locations for new roadside air pollution monitoring sites. Additionally, the capability of this tool to facilitate objective decision-making processes is evaluated by determining the influence range providing reliable air pollution information with the addition of the new monitoring sites.Methods : The proposed learning-based optimization algorithm is a new approach for selecting the new optimal monitoring sites by comprehensively considering social, economic, and environmental factors aligned with the installation purpose of the monitoring system in Seoul. The algorithm starts with genetic algorithms to select candidate locations for new monitoring sites that maximize the influence area of the expanded monitoring network compared to the existing monitoring network, capture a high overall level of air pollution, and do not overlap with the existing monitoring network. After that, PROMETHEE method is applied to evaluate the solutions generated by the genetic algorithm and choose the final solution that best fits six evaluation factors (Information entropy, number of new monitoring sites, distance from point sources, wind speed, traffic volume, and population) to be considered when installing new monitoring sites.Results and Discussion : The learning-based optimization algorithm selects 10 potential new monitoring sites adding to the existing roadside air pollution monitoring network having 15 monitoring sites. The explainable spatiotemporal range of the air pollution information that can be expected after the installation of the new monitoring sites is quantified to cover 84.33% of Seoul, reducing the uncertainty of the air pollution information of existing monitoring network by 26.15%. The final solution, selected from several solutions, can get new optimal roadside air pollution monitoring sites reflecting the regional characteristics of Seoul and the installation purpose of the monitoring system by having a small number of newly established monitoring locations, being close to air pollution emissions facilities, and having a high population and traffic volume.Conclusion : The proposed learning-based optimization method, using relevant variables for the installation purpose of the monitoring system, can derive the objective solution for deciding new monitoring locations of the roadside air pollution monitoring network, considering additional social factors as opposed to urban air pollution monitoring network. The final solution obtained through the optimization algorithm has great potential for future use, as it can guide to determine practical and feasible new monitoring sites with additional on-site verification. Furthermore, this optimized approach can be applied widely during the decision-making process for the expansion of other environmental monitoring networks.

Air Pollution in South Texas: A Short Communication of Health Risks and Implications

Air pollution is a major public health concern. The region of South Texas in the United States has experienced high levels of air pollution in recent years due to an increase in population, cross-border trade between the U.S.A. and Mexico, and high vehicular activity. This review assesses the relationships between human health and air pollution in South Texas. A thorough scientific search was performed using PubMed, Science Direct, and ProQuest, with most of the literature focusing on the source apportionment of particulate matter that is 2.5 microns or less in width (PM2.5), Carbon Dioxide (CO2), carbon monoxide (CO), Black Carbon (BC), and associated health risks for children and pregnant women. Findings from the source apportionment studies suggest the role of industries, automobiles emissions, agricultural burning, construction work, and unpaved roads in the overall deterioration of air quality and deleterious health effects, such as respiratory and cardiovascular diseases. This review demonstrates the pressing need for more air pollution and health effects studies in this region, especially the Brownsville–Harlingen–McAllen metropolitan area.

Prediction of the Concentration of Particulate Matter 2.5 Using Virtual Sensors Applied to Valle de Aburrá

Pollution in urban areas has been one of the most relevant problems of the last decade since it represents a threat to public health. Specifically, particulate matter (PM2.5) is a pollutant that causes serious health complications, such as heart and lung diseases. Centers for monitoring contaminants and climatic variables have been established to adopt measures to control the consequences of high levels of air pollution. However, these monitoring centers sometimes make decisions when pollution levels are already harmful to health, which may be related to sensor miscalibration and failures. This study presents a PM2.5 prediction system based on a state-space model—developed with real data from 2019—plus a Kalman filter to improve the prediction. The system was subsequently validated using real data captured in 2018 in Valle de Aburrá. Therefore, this is an important first step towards a more robust PM diagnosis and prediction system in the presence of false and mismatched data in the measurement.

Effect of short-term air pollution exposure on migraine: A protocol for systematic review and meta-analysis on human observational studies

BackgroundThe World Health Organization (WHO) has identified air pollution as one of the greatest environmental risks to public health. High levels of ambient air pollution are known to have adverse health effects, but the relationship between exposure to air pollutants and migraine attack has not been established. ObjectivesThis study aims to systematically review the effects of short-term exposure to fine and coarse particulate matter (PM), ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide on migraine attack. MethodsThe systematic review and meta-analysis will follow the WHO handbook for guideline development. Our protocol will comply with the guidelines of the Preferred Reporting Items for Systematic Review and meta-Analysis Protocols. Eligibility criteriaOriginal peer-reviewed studies conducted in the general population regardless of age and sex to investigate the association between short-term exposure to ambient air pollutants and migraine will be eligible for inclusion. Only time-series, case-crossover, and panel studies will be included. Information sourcesWe will search the electronic databases MEDLINE, Embase, Web of Science, Global Health, Cumulative Index to Nursing and Allied Health Literature in accordance with the pre-established search strategy. We will also check the reference list of included papers and previous reviews for supplementary search. Data synthesis methodWe will perform data extraction in accordance with the predesigned table. Using random-effects meta-analysis, we will present summary statistics (RRs and corresponding 95% CIs) associated with standardized increases in each pollutant level. Heterogeneity between studies will be assessed using 80% prediction intervals (PI). Subgroup analyzes will be performed to explore sources of heterogeneity, if any. The main findings will be presented in summary of finding table, visual display and narrative synthesis. We will review the impact of each air pollutant exposure separately. Evidence appraisalWe will employ the adaption of Grading of Recommendations, Assessment, Development, and Evaluations tool to assess the confidence in the body of evidence.

Influences of home kitchen designs on indoor air quality

Kitchen indoor air quality (IAQ) has not been well-addressed amongst other IAQ problems at home, despite the fact that cooking is one of the major home activities that can generate high levels of respirable particles and gaseous air pollutants. This study aims at investigating the effects of home kitchen designs on the performance of various ventilation strategies in reducing exposure to IAQ pollutants. The degree of natural ventilation was found to be dependent largely on the relative position of window and door opening, and using mixed ventilation with natural cross-ventilation and mechanical ventilation did not necessarily provide better ventilation. Natural ventilation with the added fume extraction by the exhaust fan could not protect the occupants from high levels of cooking pollutants. Range hood on the other hand could quickly and locally remove particles and gaseous cooking pollutants from the source. The study recommends using a range hood alone during cooking or with single-side natural ventilation to maintain an acceptable IAQ in the home kitchen.

Ambient Air Quality Assessment in Karachi, Sindh Pakistan

Pakistan’s urban air pollution is amongst the utmost severity in the world that causes acute damage to the economy and human health. This study was designed for the assessment of ambient air quality in different areas of Karachi. The data were collected as pre-monsoon and post-monsoon season from main industrial zones of Karachi, S.I.T.E area, North Karachi industrial area, Korangi industrial area and Landhi industrial area in the year of 2017-2019. These zones are also a blend of industrial, residential, commercial and heavy traffic zones with dense populations. Ambient air data were collected for different pollutants like Nitrogen Oxides (NOx), Carbon Monoxide (CO), Sulfur Dioxide (SO2) and Particulate Matters (TSPM, PM10 and PM2.5). It is observed that in pre-monsoon, there is quite high intensity of particulate matter (TSPM, PM10 & PM2.5) present in the air, whereas CO and NO2 values found in all four zones are moderate in the air samples as prescribed by Sindh Environmental Protection Agency (SEPA) standards. It is observed specifically in Landhi, S.I.T.E Area and Korangi industrial area which are more prone to the exposure of these pollutants. In post-monsoon, the intensity of particulate matters (TSPM, PM10 & PM2.5), CO and NO2 values in all four zones are less to moderate than the values of pre-monsoon due to the seasonal effects. Study data shows that three major zones; Landhi, Korangi and S.I.T.E. industrial areas are at a high exposure to gases and other toxic elements. North Karachi is at the least risk because of having a small scale of industries present. The PM10 & PM2.5 levels average about 2 - 3-fold greater than the SEPA standards. High levels of ambient air pollutants cause severe health problems and chronic diseases on human health. Therefore the implementation of rules and regulations regarding ambient air pollutants should be more rigorous.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48–69 % and 63–71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

Air pollution and decreased bone mineral density among Women's Health Initiative participants.

Osteoporosis heavily affects postmenopausal women and is influenced by environmental exposures. Determining the impact of criteria air pollutants and their mixtures on bone mineral density (BMD) in postmenopausal women is an urgent priority. We conducted a prospective observational study using data from the ethnically diverse Women's Health Initiative Study (WHI) (enrollment, September 1994-December 1998; data analysis, January 2020 to August 2022). We used log-normal, ordinary kriging to estimate daily mean concentrations of PM10, NO, NO2, and SO2 at participants' geocoded addresses (1-, 3-, and 5-year averages before BMD assessments). We measured whole-body, total hip, femoral neck, and lumbar spine BMD at enrollment and follow-up (Y1, Y3, Y6) via dual-energy X-ray absorptiometry. We estimated associations using multivariable linear and linear mixed-effects models and mixture effects using Bayesian kernel machine regression (BKMR) models. In cross-sectional and longitudinal analyses, mean PM10, NO, NO2, and SO2 averaged over 1, 3, and 5 years before the visit were negatively associated with whole-body, total hip, femoral neck, and lumbar spine BMD. For example, lumbar spine BMD decreased 0.026 (95% CI: 0.016, 0.036) g/cm2/year per a 10% increase in 3-year mean NO2 concentration. BKMR suggested that nitrogen oxides exposure was inversely associated with whole-body and lumbar spine BMD. In this cohort study, higher levels of air pollutants were associated with bone damage, particularly on lumbar spine, among postmenopausal women. These findings highlight nitrogen oxides exposure as a leading contributor to bone loss in postmenopausal women, expanding previous findings of air pollution-related bone damage. US National Institutes of Health.

Does physical activity attenuate the association between ambient PM2.5 and physical function?

BackgroundPhysical function (PF), such as muscle strength, performing daily activities, has gradually declined with the increase of age, causing the occurrence of disability and diseases burden. Air pollution exposure and physical activity (PA) were both linked to PF. We aimed to explore the individual and joint effects of particulate matter <2.5 μm (PM2.5) and PA on PF. MethodsA total of 4537 participants and 12,011 observations aged ≥45 years old from the China Health and Retirement Longitudinal Study (CHARLS) cohort from 2011 to 2015 were included into the study. PF was assessed by a combined score of four tests, including grip strength, walking speed, sense of balance, and chair standing tests. Air pollution exposure data was from The ChinaHighAirPollutants (CHAP) dataset. The annual PM2.5 exposure for each individual was estimated based on county-level resident addresses. We estimated the volume of moderate-to-vigorous physical activity (MVPA) by quoting metabolic equivalent (MET). Multivariate linear model was conducted for baseline analysis, and linear mixed model with random participant intercepts was constructed for cohort longitudinal analysis. ResultsPM2.5 was negatively associated with PF, while PA was positively associated with PF in baseline analysis. In cohort longitudinal analysis, a 10 μg/m3 increase in PM2.5 was associated to a 0.025 point (95 % CI: −0.047, −0.003) decrease in PF score, and a 10-MET-h/week increase in PA was related to a 0.004 point (95 % CI: 0.001, 0.008) increase in PF score. The association between PM2.5 and PF decreased by increased PA intensity, and PA reversed the detrimental effects between PM2.5 and PF. ConclusionPA attenuated the association of air pollution with PF at both high and low levels of air pollution, implying that PA may be an effective behavior to reduce the adverse effects of poor air quality on PF.

Effect of residential relocation on environmental exposures in European cohorts: An exposome-wide approach

Residential relocation is increasingly used as a natural experiment in epidemiological studies to assess the health impact of changes in environmental exposures. Since the likelihood of relocation can be influenced by individual characteristics that also influence health, studies may be biased if the predictors of relocation are not appropriately accounted for. Using data from Swedish and Dutch adults (SDPP, AMIGO), and birth cohorts (BAMSE, PIAMA), we investigated factors associated with relocation and changes in multiple environmental exposures across life stages.We used logistic regression to identify baseline predictors of moving, including sociodemographic and household characteristics, health behaviors and health. We identified exposure clusters reflecting three domains of the urban exposome (air pollution, grey surface, and socioeconomic deprivation) and conducted multinomial logistic regression to identify predictors of exposome trajectories among movers.On average, 7 % of the participants relocated each year. Before relocating, movers were consistently exposed to higher levels of air pollution than non-movers. Predictors of moving differed between the adult and birth cohorts, highlighting the importance of life stages. In the adult cohorts, moving was associated with younger age, smoking, and lower education and was independent of cardio-respiratory health indicators (hypertension, BMI, asthma, COPD). Contrary to adult cohorts, higher parental education and household socioeconomic position were associated with a higher probability of relocation in birth cohorts, alongside being the first child and living in a multi-unit dwelling. Among movers in all cohorts, those with a higher socioeconomic position at baseline were more likely to move towards healthier levels of the urban exposome.We provide new insights into predictors of relocation and subsequent changes in multiple aspects of the urban exposome in four cohorts covering different life stages in Sweden and the Netherlands. These results inform strategies to limit bias due to residential self-selection in epidemiological studies using relocation as a natural experiment.

Health effects of exposure to particulate matter and physical activity by individual sustainable commuting modes

Promotion of sustainable commuting modes (e.g., walking, cycling and electric mobility) is considered as a promising solution to achieve sustainability. Active travel may also increase physical activity, which is beneficial for health. But it increases health burdens by directly exposing commuters to air pollution. There is a lack of generalizable quantitative approaches to comprehensively evaluate the health outcomes of sustainable plans during the design period. In this research, we aim to examine the long-term combined health impacts of commuting by walking, cycling, and e-moped in Cangzhou, China, a populous industrial city with high air pollution levels, to inform decision-making prior to the implementation of sustainable strategies. A taxi-based mobile remote sensing system was implemented to create a link-level fine particulate matter (PM2.5) concentration map to overcome the insufficiency of sparse fixing monitoring network, which allows researchers to determine joint relative risks of all-cause mortality at a finer spatial scale. Results show that the benefits always outweighed the risks for active commuting at the case study site, even in high polluted days. E-moped commuters experienced a negative burden. In our case, encouraging these commuters to travel during off-peak periods to reduce exposure might not be effective and could even lead to greater PM2.5 exposure than during peak periods (up to 0.37 μg/km). The proposed method can be applied to different locations, providing decision makers with the information needed to target interventions to achieve sustainability.

Modeling of COVID‐19 death rate using various air pollutants: A multiple linear regression approach

AbstractAir pollution is a significant health risk, especially for vulnerable populations such as children, people with chronic illnesses, the elderly, and the economically and socially disadvantaged. Furthermore, air pollution has enormous social costs that we all bear in the form of premature deaths, low productivity, sick leave, and other strains on the healthcare system. The primary sources of air pollution are traffic, home fires, and industry. Measuring NO2 levels in air pollution reveals the extent of pollution caused by traffic, particularly diesel vehicles, which are the primary source of NO2. COVID‐19 rates are rising in areas with high levels of air pollution, according to mounting evidence. Toxic contaminants can make people more susceptible to COVID‐19. The causal relationship between air pollution and COVID‐19 cases has yet to be established, but experts warn that long‐term exposure will undoubtedly make people more susceptible to lung infections. Air pollution has been linked to an increase in cancer, heart disease, stroke, diabetes, asthma, and other comorbidities by inducing cellular damage and inflammation throughout the body. All of these factors increase the risk of death in COVID‐19 patients. As a result, air quality parameters must be predicted and monitored. To predict results, this study proposes a statistical‐based machine learning approach. Using multiple linear regression (MLR), Decision Tree (D.T.), and Random Forest (R.F.), the experimental results achieved 80%, 73%, and 65% accuracy on the dataset, respectively.