Air Pollution Exposure Assessment Research Articles

Exposure to ambient air pollution and cognitive function: an analysis of the English Longitudinal Study of Ageing cohort

BackgroundAn increasing number of studies suggest adverse effects of exposure to ambient air pollution on cognitive function, but the evidence is still limited. We investigated the associations between long-term exposure to air pollutants and cognitive function in the English Longitudinal Study of Ageing (ELSA) cohort of older adults.MethodsOur sample included 8,883 individuals from ELSA, based on a nationally representative study of people aged ≥ 50 years, followed-up from 2002 until 2017. Exposure to air pollutants was modelled by the CMAQ-urban dispersion model and assigned to the participants’ residential postcodes. Cognitive test scores of memory and executive function were collected biennially. The associations between these cognitive measures and exposure to ambient concentrations of NO2, PM10, PM2.5 and ozone were investigated using mixed-effects models adjusted for time-varying age, physical activity and smoking status, as well as baseline gender and level of education.ResultsIncreasing long-term exposure per interquartile range (IQR) of NO2 (IQR: 13.05 μg/m3), PM10 (IQR: 3.35 μg/m3) and PM2.5 (IQR: 2.7 μg/m3) were associated with decreases in test scores of composite memory by -0.10 (95% confidence interval [CI]: -0.14, -0.07), -0.02 [-0.04, -0.01] and -0.08 [-0.11, -0.05], respectively. The same increases in NO2, PM10 and PM2.5 were associated with decreases in executive function score of -0.31 [-0.38, -0.23], -0.05 [-0.08, -0.02] and -0.16 [-0.22, -0.10], respectively. The association with ozone was inverse across both tests. Similar results were reported for the London-dwelling sub-sample of participants.ConclusionsThe present study was based on a long follow-up with several repeated measurements per cohort participant and long-term air pollution exposure assessment at a fine spatial scale. Increasing long-term exposure to NO2, PM10 and PM2.5 was associated with a decrease in cognitive function in older adults in England. This evidence can inform policies related to modifiable environmental exposures linked to cognitive decline.

The Association Between Personal Air Pollution Exposures and Fractional Exhaled Nitric Oxide (FeNO): A Systematic Review.

Airway inflammation is a common biological response to many types of environmental exposures and can lead to increased nitric oxide (NO) concentrations in exhaled breath. In recent years, several studies have evaluated airway inflammation using fractional exhaled nitric oxide (FeNO) as a biomarker of exposures to a range of air pollutants. This systematic review aims to summarize the studies that collected personal-level air pollution data to assess the air pollution-induced FeNO responses and to determine if utilizing personal-level data resulted in an improved characterization of the relationship between air pollution exposures and FeNO compared to using only ambient air pollution exposure data. Thirty-six eligible studies were identified. Overall, the studies included in this review establish that an increase in personal exposure to particulate and gaseous air pollutants can significantly increase FeNO. Nine out of the 12 studies reported statistically significant FeNO increases with increasing personal PM2.5 exposures, and up to 11.5% increase in FeNO per IQR increase in exposure has also been reported between FeNO and exposure to gas-phase pollutants, such as ozone, NO2, and benzene. Furthermore, factors such as chronic respiratory diseases, allergies, and medication use were found to be effect modifiers for air pollution-induced FeNO responses. About half of the studies that compared the effect estimates using both personal and ambient air pollution exposure methods reported that only personal exposure yielded significant associations with FeNO response. The evidence from the reviewed studies confirms that FeNO is a sensitive biomarker for air pollutant-induced airway inflammation. Personal air pollution exposure assessment is recommended to accurately assess the air pollution-induced FeNO responses. Furthermore, comprehensive adjustments for the potential confounding factors including the personal exposures of the co-pollutants, respiratory disease status, allergy status, and usage of medications for asthma and allergies are recommended while assessing the air pollution-induced FeNO responses.

Bioindicators and human biomarkers as alternative approaches for cost-effective assessment of air pollution exposure

Traditional methods of air pollution monitoring require substantial investment in equipment and infrastructure. However, efficient and cost-effective alternatives offer promising solutions for region-specific pollution assessments and understanding their impact on local populations. This review explores examples of low-cost monitoring methods, focusing on natural bioindicators, human interaction-based techniques, and the outcomes associated with air pollution exposure. Bioindicators such as spider webs, lichens, mosses, and Tradescantia pallida (T. pallida) are discussed as potential tools for air pollution monitoring. Human biomonitoring techniques, including the micronucleus assay and the assessment of pulmonary anthracosis, are examined for their ability to provide valuable insights into genotoxic effects and long-term exposure. The advantages and limitations of each method are highlighted. The review advocates for continued research and development to refine these approaches, with the aim of mitigating the adverse health impacts of air pollution on both individuals and communities.

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980-2019.

Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-based aerosol optical depth before 2000, has resulted in a gap in spatiotemporally resolved PM2.5 data for historical periods. Taking the United Kingdom as an example, we leveraged the light gradient boosting model to capture the spatiotemporal association between PM2.5 concentrations and multi-source geospatial predictors. Augmented PM2.5 from PM10 measurements expanded the spatiotemporal representativeness of the ground measurements. Observations before and after 2009 were used to train and test the models, respectively. Our model showed fair prediction accuracy from 2010 to 2019 [the ranges of coefficients of determination (R2) for the grid-based cross-validation are 0.71-0.85] and commendable back extrapolation performance from 1998 to 2009 (the ranges of R2 for the independent external testing are 0.32-0.65) at the daily level. The pollution episodes in the 1980s and pollution levels in the 1990s were also reproduced by our model. The 4-decade PM2.5 estimates demonstrated that most regions in England witnessed significant downward trends in PM2.5 pollution. The methods developed in this study are generalizable to other data-rich regions for historical air pollution exposure assessment.

Low-cost IoT sensors reveal adverse health outcomes associated with air pollution exposure

Abstract Background Air pollution is a major public health threat and has been linked to various adverse health effects including respiratory and cardiovascular diseases. Fine particulate matter (PM2.5) and coarse particulate matter (PM10) are two major components of air pollution that are significant contributors to these health outcomes. With the advent of low-cost sensors, accurate measurement of exposure has become possible, allowing for better assessment of the impact of air pollution exposure on human health. Methods To investigate the association between air pollution exposure and illnesses resulting in hospital visits, IoT sensors were deployed in Ulaanbaatar, Mongolia, to measure PM2.5 and PM10 levels from 2018-2021. Health data was obtained from the Mongolian National Center for Public Health. Multivariable logistic regression models were used to estimate overall odds ratios for the associations between daily PM2.5 and PM10 exposure and the occurrence of illnesses, adjusting for age and sex. Results The unadjusted odds ratio for the association between PM10 exposure and acute respiratory illnesses was 1.3, while the unadjusted odds ratio for PM2.5 exposure was 1.2; decreasing to 1.2 and 1.1 respectively after adjusting for age and sex (p < 0.05). These findings are consistent with previous studies and highlight the significant impact of air pollution on the occurrence of acute respiratory illnesses. The use of IoT sensors allowed for accurate assessment of air pollution exposure in the study population. Conclusions This study provides further evidence of the detrimental effects of air pollution on human health, particularly on the occurrence of acute respiratory illnesses. These findings highlight the importance of continued efforts to reduce air pollution levels. Accurate measurement of exposure rates using IoT sensors can aid in identifying and addressing sources of air pollution to improve the quality of life for individuals and communities affected by this health threat. Key messages • This study shows the significant impact of air pollution on acute respiratory illnesses, highlighting the importance of reducing air pollution levels. • Accurate measurement of air pollution exposure using IoT sensors can aid in identifying and addressing sources of air pollution to improve public health.

Biomonitoring of Atmospheric PAHs and PMs Using Xanthoria parietina and Cupressus sempervirens in Bouira (Algeria)

Air pollution constitutes a major environmental risk factor for living beings. Protection against such risk needs air pollution monitoring and control. Air pollution monitoring can be obtained in several ways. Amongst them, passive methods assessing cumulative exposure are of particular interest. A passive approach consisting of ambient concentrations biomonitoring of polycyclic aromatic hydrocarbons (PAHs) using lichens and plants was used for assessment of ambient air pollution exposure in the industrial region of Oued El Berdi in Bouira (Algeria). Seven stations were chosen to take samples of lichen thalli of Xanthoria parietina and conifer scales and barks of Cupressus sempervirens in April 2018. The physiological parameters of the chlorophyll and the proline content were measured, and the atmospheric PAHs and particulate matter (PM) concentrations were quantified. The results show a spatial variation between the different stations and directions. The PAH concentrations accumulated in lichen range from 35 ± 3 ng/g dw to 2222 ± 376 ng/g dw and show significant differences (p = 0.017). These concentrations are higher than those found in conifer scales (18.8 ± 7 dw to 1183.5 ± 876 ng/g dw) and that found in conifer barks (7 ± 3 dw to 515.3 ± 19 ng/g dw). Significant difference between the reference stations of Tikjda and Errich and the five industrial stations of Oued El Berdi were also observed. Physiological parameters (chlorophyll a, chlorophyll b, chlorophyll ab) and proline and air pollutants accumulated (PAHs and PM) were associated. Biomonitoring allowed to show that the industrial area of Oued El Berdi was impacted by PAHs and PM, which are generated mainly by factories located there.

A Comparison Study on Multiple Modeling Approaches for Air Pollutant Geographic Model Development in Shanghai

Geostatistical models have been widely used in the exposure assessment of ambient air pollutants. However, few studies have focused on comparisons of modeling approaches and their prediction results. Here, we collected the NO2 and PM2.5 monitoring data from 55 sites in Shanghai from 2016 to 2019 and the geographic variables, such as road network, points of interest of emission locations, and satellite data were included. We used partial least squares regression (PLS), supervised linear regression (SLR), and random forest (RF) algorithms to develop spatial models and used ordinary kriging (OK) to develop a two-step model. We evaluated the models using a 5-fold cross validation method and selected the best model structure for each modeling approach between one-or two-step models that had been developed with or without OK. The results revealed that the best NO2 models were the RF-OK (Rmse2 was 0.70-0.82) and PLS-OK (Rmse2 was 0.78-0.84) models; the PLS model for PM2.5(Rmse2 was 0.62-0.71) outperformed the other PM2.5 models. We used the best models to predict annual exposures in Shanghai at a 1 km spatial scale and conducted the correlation analysis among the predictions of the best models. The results demonstrated that the NO2 predictions had higher correlation coefficients (r was 0.82-0.91) compared with those of the PM2.5 models (r was 0.66-0.96). Based on the exposure results predicted using the three models in 2019, we evaluated the cumulative population exposure concentrations for NO2 and PM2.5 in Shanghai.

Assessment of Household Air Pollution Exposure in Kampala Capital City, Uganda

Assessment of Household Air Pollution Exposure in Kampala Capital City, Uganda

Particle exposure and cytotoxicity assessment of indoor air pollution from cooking emissions in a low-income home

Particle exposure and cytotoxicity assessment of indoor air pollution from cooking emissions in a low-income home

The pregnancy research on inflammation, nutrition, & city environment: systematic analyses study (PRINCESA) cohort, 2009-2015.

The Pregnancy Research on Inflammation, Nutrition, & City Environment: Systematic Analyses Study (PRINCESA) cohort was set up to evaluate associations between air pollution and birth outcomes among pregnant persons in Mexico City. Specifically, the study was designed to improve air pollution exposure assessment and elucidate biological mechanisms underlying associations between maternal exposures and adverse pregnancy outcomes. Pregnant persons (all women) (N = 935) between ages 18-45 who lived and/or worked in metropolitan Mexico City, Mexico, from 2009 to 2015 and liveborn singleton infants (N = 815) of participants who completed follow-up were enrolled in the cohort. We followed participants monthly from enrollment to delivery and the following categories of data were obtained: demographic, medical and obstetric history, geo-referenced data, repeated measures on daily activity patterns, reported food intake, anthropometric, clinical and obstetric data, 20 serum and 20 cervicovaginal cytokines, and lower reproductive tract infection. Repeated ultrasound measures of fetal parameters and infant birth data are also included in the study's database. In addition, PRINCESA investigators calculated air pollution exposure measures for six pollutants measured by the Mexico City Atmospheric Monitoring System (SIMAT). These estimates utilize participants' addresses to account for spatial variation in exposure (nearest monitor, inverse distance weighting, and kriging) and are available daily during pregnancy for participants. To date, associations between environmental and nutritional impacts on maternal and child health outcomes have been evaluated. PRINCESA has a comprehensive database of maternal and infant data and biological samples and offers collaboration opportunities to study associations between environmental and other factors, including nutrition and pregnancy outcomes.

ADS-B data usage for aircraft noise and air quality modelling and measurement during specific stages of LTO cycle

The paper is targeted at the analysis of the importance of Automatic Dependent Surveillance-Broadcast (ADS-B) data in the terms of overcoming gaps between aircraft noise and local air quality modelling results and their short or long-term measurements. Presented results based on noise and air pollution measurement campaign at Ukrainian airports describe general peculiarities of the pre-processing and usage of ADS-B data during specific stages of aircraft landing and taking-off (LTO) cycle in the airport. The outcomes could be used for more accurate noise and air pollution exposure assessment and the development of recommendations for noise and local air quality monitoring systems in airports under consideration.

Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data.

A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers. In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs. We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants. The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively. Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models.

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

Reliable prediction of sensory irritation threshold values of organic compounds using new models based on linear free energy relationships and GC×GC retention parameters

The human sensory irritation threshold (SIT) is an important biochemical parameter for the exposure assessment of organic air pollutants. First, we recalibrated the Abraham solvation models (ASMs) for 9 SIT endpoints by curating 720 individual experimental SIT values to find an accurate and parsimonious ASM variant, which exhibited root mean square error (RMSE) = 0.174–0.473 log unit. Second, we report linear free energy relationships — henceforth called partition models (PMs) — which exploit the correlations of 9 SIT endpoints with the linear combinations of partition coefficients for octanol-water and air-water systems showing RMSE = 0.221–0.591 log unit. These PMs can easily be integrated into widely used EPI-Suite™ screening tool. The explanatory and predictive performance of PMs were like parameter-intensive ASMs. Third, we present GC × GC models that are based on the retention times of the nonpolar analytes on the comprehensive two-dimensional gas chromatography (GC × GC), which successfully described the SIT variance (R2=0.959−0.996) and depicted a strong predictive power (RMSE = 0.359–0.660 log unit) for an independent set of nonpolar analytes. Taken together, PMs allow easy SIT screening of organic chemicals compared to ASMs. Unlike ASMs, our GC × GC models can be applied to estimate SIT of complex nonpolar mixtures.

Neighborhood Emission Mapping Operation (NEMO): A 1-km anthropogenic emission dataset in the United States

We present an unprecedented effort to map anthropogenic emissions of air pollutants at 1 km spatial resolution in the contiguous United States (CONUS). This new dataset, Neighborhood Emission Mapping Operation (NEMO), is produced at hourly intervals based on the United States Environmental Protection Agency (US EPA) National Emission Inventories 2017. Fine-scale spatial allocation was achieved through distributing the emission sources using 108 spatial surrogates, factors representing the portion of a source in each 1 km grid. Gaseous and particulate pollutants are speciated into model species for the Carbon Bond 6 chemical mechanism. All sources are grouped in 9 sectors and stored in NetCDF format for air quality models, and in shapefile format for GIS users and air quality managers. This dataset shows good consistency with the USEPA benchmark dataset, with a monthly difference in emissions less than 0.03% for any sector. NEMO provides the first 1 km mapping of air pollution over the CONUS, enabling new applications such as fine-scale air quality modeling, air pollution exposure assessment, and environmental justice studies.

Exposure Assessment of Air Pollution in Lungs

In this article, a comprehensive literature survey on air pollution and its effects on the human respiratory system is carried out. Based on the knowledge gaps, a computational assessment is proposed to find the impact of air quality on respiratory suspended particulate matter (RSPM) deposition in the human airways. A realistic 3D geometric model of the human airway was constructed to study the airflow characteristics and RSPM (PM2.5 and PM10) transport and deposition in it for normal and moderate inhalation patterns (corresponding to natural breathing) of air having an unhealthy air quality index (AQI). The results identify inertial impact as the primary mechanism of particle deposition in the human airways. They also reveals the significant differences in the deposition patterns of PM2.5 and PM10 in the right and left bronchial airways.

Integrating statistical and agent-based modelling for activity-based ambient air pollution exposure assessment

Assessment of long-term human exposure to spatiotemporally highly variable air pollution requires accounting for human space–time activity. Individual exposure and space–time track data are not available over large populations and for long periods and a modelling approach is required. However, activity-based exposure models face here challenges in setting up the model and overly-large computations. Aiming for long-term and large-population simulations, we propose an activity model which integrates statistical and agent-based modelling by treating mobility-related variables as random variables. Probability distributions for these variables are estimated or derived from mobility datasets containing observed activities. On top of the activity model, we implemented an exposure model. A case-study of exposure assessment was developed using hourly air pollution maps. The activity model can potentially integrate any mobility data and is thus applicable when limited time activity data is available at the individual level.

PM2.5, and soot measurements in Gqeberha municipality, South Africa

Background and aim: Air pollution is a major risk factor in Africa. Air pollution exposure assessment studies are lacking in South Africa and Africa, especially on fine particulate matter (PM2.5) and its chemical composition. The aim of this study was to assess the PM2.5 and soot levels in Gqeberha municipality, Eastern Cape province, South Africa Method: The study was conducted in an urban area located in Gqeberha municipality. Gravimetric analysis was used to determine PM2.5 levels from May 2021 to January 2022. Soot measurements were done using a smoke stain reflectometer. All analysis was done in the air quality laboratory at the School of Health Systems and Public Health (SHSPH) at the University of Pretoria. Results: The average PM2.5 level during the study period was 4.97± 2.99 µg/m3 which was lower than the annual WHO air quality guideline (5 µg/m3) and lower than the annual South African air quality standard (20 µg/m3). The mean soot level was 1.07 10-7m-1. Conclusion: This baseline study shows that background level PM2.5 levels in Gqeberha municipality. Although these levels are relatively low, further studies in the area are still needed. Especially that evidence shows that low levels of PM2.5 can lead to adverse health effects. Keywords: Air pollution, PM2.5, South Africa, black carbon, soot

Harmonization of Epidemiologic Research Methods to Address the Environmental and Social Determinants of Urban Slum Health Challenges in Sub-Saharan Africa.

Sub-Saharan Africa (SSA) has a significant proportion of populations living in urban slum conditions, where exposure to multiple environmental stressors and social inequalities is ubiquitous. This commentary synthesizes commonalities in recent environmental health studies from urban cities in East and West Africa, presented during a symposium sponsored by the Africa Chapter of the International Society of Environmental Epidemiology (ISEE) in August 2020. A key takeaway from this symposium is the need for harmonization of epidemiologic and exposure data collection in three domains tailored to the SSA context: (1) improvements in socioeconomic status (SES) measurement through harmonization in the conceptualization and operationalization of SES indicators; (2) improvements in air pollution exposure assessment in resource-constrained contexts by better integration, validation, and harmonization of exposure data of air pollution and mitigating factors; and (3) harmonization in the assessment of health outcomes and biomonitoring of contaminants. Focusing on these three domains would galvanize environmental epidemiologists in SSA around shared data collection instruments and shared data platforms and facilitate the pooling of data across the continent. Fostering this collaborative research will enable researchers and decision-makers to glean new insights and develop robust environmental health interventions and policies for SSA urban slums and for improved population health.

Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment.

Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces. We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model. The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts. Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.