Air pollution exposure assessment methods utilized in epidemiological studies

Distance-based indicators for evaluating environmental multi-contamination and related exposure: how far should we go?

Assessing environmental exposure to pollution is a challenging task, and scientists often use distance-based or proximity indicators when field or modeled data are unavailable. Although buffers are commonly used to represent the impact of a pollution source on neighboring populations, they can result in high-exposure misclassification. Euclidean distance-based indicators offer a promising alternative, but practices vary significantly in the literature. In this study, we aimed to compare several distance-based indicators for multiple environmental contaminants in an industrial and urban area. At the population's grid cell resolution of 200 × 200 m, we compared the distance to the closest source, the average or median distance to all sources, or a restricted number of nearby sources for six types of sources (industries, railways, rail areas, roadways, road crossings, and agricultural patches) against environmental contamination data (PM10, NO2, and multimetallic contamination in lichens). Our findings revealed that the representativeness of contamination by indicators is significantly affected by the type and number of nearby sources considered. Specifically, we found that considering the distance to the nearest source or the average distance to all sources can lead to exposure misclassifications. The optimal correlation between distance indicators and pollutant levels was observed when considering 10-14 of the closest industrial sources, located within a 4.9- to 5.5-km radius. For rail areas, the optimal number was two to three sources within a 5.4- to 7.4-km radius. For main roads, intersections, and railways, the optimal number of sources varied depending on the pollutant, generally falling within a 3- to 9.4-km radius. Environmental contamination is influenced by the diversity of nearby sources, and considering only one source increases the risk of misclassification. Our results suggest that proximity models are still appropriate for study areas where the etiology of existing health effects is unclear, providing an exploratory analysis before more sophisticated research.

Internal exposure potential of water-soluble organic molecules in urban PM2.5 evaluated by non-covalent adductome of human serum albumin

Water-soluble organic molecules (WSOMs) in inhaled PM2.5 can readily translocate from the lungs into the blood circulation, facilitating their distribution to and health effects on distant organs and tissues in the human body. Human serum albumin (HSA), the most abundant protein carrier in the blood, readily binds exogenous substances to form non-covalent adducts and subsequently transports them throughout the circulatory system, thereby indicating their internal exposure. The direct internal exposure of WSOMs in PM2.5 needs to be understood. In this study, the non-covalent HSA-WSOM adductome was developed as a dosimeter to evaluate the internal exposure potential of WSOMs in urban PM2.5. The WSOM composition was acquired from non-target high-resolution mass spectrometry analysis coupled with multiple ionizations. The binding level of HSA-WSOM non-covalent adducts was obtained from surface plasma resonance. Machine learning combined WSOM composition and the binding level of HSA-WSOM non-covalent adducts to screen bindable (also internalizable) WSOMs. The concentration of WSOM ranged from 4 to 13 μg/m3 during our observation period. Of the 17,513 mass spectral features detected, 9,484 contributed to the non-covalent adductome and possessed the internal exposure potential. 102 major contributors accounted for 90.6 % of the HSA-WSOM binding level. The fraction of internalizable WSOMs in PM2.5 varied from 11.9 % to 61.3 %, averaging 26.2 %. WSOMs that have internal exposure potential were primarily lignin-like and lipid-like substances. The HSA-WSOMs non-covalent adductome represents direct internal exposure potential, which can provide crucial insights into the molecular diagnosis of PM2.5 exposure and precise assessments of PM2.5 health effects.

Industrial air pollution and low birth weight in New Mexico, USA

In recent decades, the low birth weight (LBW) rate in New Mexico has consistently exceeded the Unites States average. Maternal exposure to air pollution during pregnancy may be a significant contributor to LBW in offspring. This study investigated the links between maternal residential exposure to air pollution from industrial sources and the risk of LBW in offspring. The analysis included 22,375 LBW cases and 233,340 controls. It focused on 14 common chemicals listed in the Toxic Release Inventory (TRI) and monitoring datasets, which have abundant monitoring samples. The Emission Weighted Proximity Model (EWPM) was used to calculate maternal air pollution exposure intensity. Adjusted odds ratios (adjORs) were calculated using binary logistic regressions to examine the association between maternal residential air pollution exposure and LBW, while controlling for potential confounders, such as the maternal age, race/ethnicity, gestational age, prenatal care, education level, consumption of alcohol during pregnancy, public health regions, child's sex, and the year of birth. Multiple comparison correction was applied using the False Discovery Rate approach. The results showed that maternal residential exposure to 1,2,4-trimethylbenzene, benzene, chlorine, ethylbenzene, and styrene had significant positive associations with LBW in offspring, with adjusted odds ratios ranging from 1.10 to 1.13. These five chemicals remained as significant risk factors after dividing the estimated exposure intensities into four categories. In addition, significant linear trends were found between LBW and maternal exposure to each of the five identified chemicals. Furthermore, 1,2,4-trimethylbenzene was identified as a risk factor to LBW for the first time. The findings of this study should be confirmed through additional epidemiological, biological, and toxicological studies.

Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health

Background

The intricate interplay between human well-being and the surrounding environment underscores contemporary discourse. Within this paradigm, comprehensive environmental monitoring holds the key to unraveling the intricate connections linking population health to environmental exposures. The advent of satellite remote sensing monitoring (SRSM) has revolutionized traditional monitoring constraints, particularly limited spatial coverage and resolution. This innovation finds profound utility in quantifying land covers and air pollution data, casting new light on epidemiological and geographical investigations. This dynamic application reveals the intricate web connecting public health, environmental pollution, and the built environment.

Objective

This comprehensive review navigates the evolving trajectory of SRSM technology, casting light on its role in addressing environmental and geographic health issues. The discussion hones in on how SRSM has recently magnified our understanding of the relationship between air pollutant exposure and population health. Additionally, this discourse delves into public health challenges stemming from shifts in urban morphology.

Methods

Utilizing the strategic keywords "SRSM," "air pollutant health risk," and "built environment," an exhaustive search unfolded across prestigious databases including the China National Knowledge Network (CNKI), PubMed and Web of Science. The Citespace tool further unveiled interconnections among resultant articles and research trends.

Results

Synthesizing insights from a myriad of articles spanning 1988 to 2023, our findings unveil how SRMS bridges gaps in ground-based monitoring through continuous spatial observations, empowering global air quality surveillance. High-resolution SRSM advances data precision, capturing multiple built environment impact factors. Its application to epidemiological health exposure holds promise as a pioneering tool for contemporary health research.

Conclusion

This review underscores SRSM's pivotal role in enriching geographic health studies, particularly in atmospheric pollution domains. The study illuminates how SRSM overcomes spatial resolution and data loss hurdles, enriching environmental monitoring tools and datasets. The path forward envisions the integration of cutting-edge remote sensing technologies, novel explorations of urban-public health associations, and an enriched assessment of built environment characteristics on public well-being.

A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure

Environmental epidemiology studies require accurate estimations of exposure intensities to air pollution. The process from air pollutant emission to individual exposure is however complex and nonlinear, which poses significant modeling challenges. This study aims to develop an exposure assessment model that can strike a balance between accuracy, complexity, and usability. In this regard, neural networks offer one possible approach. This study employed a custom-designed pruned feed-forward neural network (pruned-FNN) approach to calculate the air pollution exposure index based on emission time and rates, terrain factors, meteorological conditions, and proximity measurements. The model's performance was evaluated by cross-validating the estimated exposure indexes with ground-based monitoring records. The pruned FNN can predict pollution exposure indexes (PEIs) that are highly and stably correlated with the monitored air pollutant concentrations (Spearman's rank correlation coefficients for tenfold cross-validation (mean ± standard deviation: 0.906 ± 0.028) and for random cross-validation (0.913 ± 0.024)). The predicted values are also close to the ground truth in most cases (95.5% of the predicted PEIs have relative errors smaller than 10%) when the training datasets are sufficiently large and well-covered. The pruned-FNN method can make accurate exposure estimations using a flexible number of variables and less extensive data in a less money/time-consuming manner. Compared to other exposure assessment models, the pruned FNN is an appropriate and effective approach for exposure assessment that covers a large geographic area over a long period of time.

Large-scale agricultural burning and cardiorespiratory emergency department visits in the U.S. state of Kansas

BACKGROUND

Prescribed agricultural burning is a common land management practice, but little is known about the health effects from the resulting smoke exposure.

OBJECTIVE

To examine the association between smoke from prescribed burning and cardiorespiratory outcomes in the U.S. state of Kansas.

METHODS

We analyzed a zip code-level, daily time series of primary cardiorespiratory emergency department (ED) visits for February-May (months when prescribed burning is common in Kansas) in the years 2009-2011 (n = 109,220). Given limited monitoring data, we formulated a measure of smoke exposure using non-traditional datasets, including fire radiative power and locational attributes from remote sensing data sources. We then assigned a population-weighted potential smoke impact factor (PSIF) to each zip code, based on fire intensity, smoke transport, and fire proximity. We used Poisson generalized linear models to estimate the association between PSIF on the same day and in the past 3 days and asthma, respiratory including asthma, and cardiovascular ED visits.

RESULTS

During the study period, prescribed burning took place on approximately 8 million acres in Kansas. Same-day PSIF was associated with a 7% increase in the rate of asthma ED visits when adjusting for month, year, zip code, meteorology, day of week, holidays, and correlation within zip codes (rate ratio [RR]: 1.07; 95% confidence interval [CI]: 1.01, 1.13). Same-day PSIF was not associated with a combined outcome of respiratory ED visits (RR [95% CI]: 0.99 [0.97, 1.02]), or cardiovascular ED visits (RR [95% CI]: 1.01 [0.98, 1.04]). There was no consistent association between PSIF during the past 3 days and any of the outcomes.

SIGNIFICANCE

These results suggest an association between smoke exposure and asthma ED visits on the same day. Elucidating these associations will help guide public health programs that address population-level exposure to smoke from prescribed burning.

Personal and community-level exposure to air pollution and daily changes in respiratory symptoms and oxygen saturation among adults with COPD

Background

Air pollution exposure is associated with hospital admissions for Chronic Obstructive Pulmonary Disease (COPD). Few studies have investigated whether daily personal exposure to air pollutants affects respiratory symptoms and oxygenation among COPD patients.

Methodology

We followed 30 former smokers with COPD for up to 4 non-consecutive 30-day periods in different seasons. Participants recorded worsening of respiratory symptoms (sub-categorized as breathing or bronchitis symptoms) by daily questionnaire, and oxygen saturation by pulse oximeter. Personal and community-level exposure to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) were measured by portable air quality monitors and stationary monitors in the Boston area. We used generalized and multi-level linear mixed-effects models to estimate associations of the 24-hour average of each pollutant in the previous day with changes in respiratory symptoms and oxygen saturation.

Results

Higher community-level exposure to air pollutants was associated with worsening respiratory symptoms. An interquartile range (IQR) higher community-level O₃ was associated with a 1.35 (95%CI: 1.07-1.70) higher odds of worsening respiratory symptoms. The corresponding ORs for community-level PM_2.5 and NO₂ were 1.18 (95%CI: 1.02-1.37) and 1.06 (95%CI: 0.90-1.25), respectively. Community-level NO₂ was associated with worsening bronchitis symptoms (OR=1.25, 95%CI: 1.00-1.56), but not breathing symptoms. Personal PM_2.5 exposure was associated with lower odds of worsening respiratory symptoms (OR=0.91; 95%CI: 0.81-1.01). Personal exposure to NO₂ was associated with 0.11% lower oxygen saturation (95%CI: -0.22, 0.00) per IQR.

Conclusions

In this COPD population, there was a pattern of worsening respiratory symptoms associated with community-level exposure to O₃ and PM_2.5, and worsening oxygenation associated with personal exposure to NO₂.

Long-term exposure and health risk assessment from air pollution: impact of regional scale mobility

BACKGROUND

The negative effect of air pollution on human health is widely reported in recent literature. It typically involves urbanized areas where the population is concentrated and where most primary air pollutants are produced. A comprehensive health risk assessment is therefore of strategic importance for health authorities.

METHODS

In this study we propose a methodology to perform an indirect and retrospective health risk assessment of all-cause mortality associated with long-term exposure to particulate matter less than 2.5 microns (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in a typical Monday to Friday working week. A combination of satellite-based settlement data, model-based air pollution data, land use, demographics and regional scale mobility, allowed to examine the effect of population mobility and pollutants daily variations on the health risk. A Health Risk Increase (HRI) metric was derived on the basis of three components: hazard, exposure and vulnerability, utilizing the relative risk values from the World Health Organization. An additional metric, the Health Burden (HB) was formulated, which accounts for the total number of people exposed to a certain risk level.

RESULTS

The effect of regional mobility patterns on the HRI metric was assessed, resulting in an increased HRI associated with all three stressors when considering a dynamic population compared to a static one. The effect of diurnal variation of pollutants was only observed for NO2 and O3. For both, the HRI metric resulted in significantly higher values during night. Concerning the HB parameter, we identified the commuting flows of the population as the main driver in the resulting metric.

CONCLUSIONS

This indirect exposure assessment methodology provides tools to support policy makers and health authorities in planning intervention and mitigation measures. The study was carried out in Lombardy, Italy, one of the most polluted regions in Europe, but the incorporation of satellite data makes our approach valuable for studying global health.

Exposure to ambient gaseous air pollutants and adult lung function: a systematic review

Exposure to hazardous air pollutants is identified as most obvious premature mortality factors in the world. Numerous epidemiological studies have estimated exposure to air pollutants may cause pulmonary toxicity and the incidence of respiratory diseases including chronic obstructive pulmonary disease (COPD), chronic bronchitis and asthma. The currently research was performed to evaluation the association between gaseous pollutants and lung function in healthy adults. Articles related to this study were selected from researches of Scopus, PubMed, and Web of Science databases. A total of 2,644 articles were retrieved and 39 records were reviewed after removing duplicates and excluding irrelevant studies. The result of this systematic review indicated that there is some evidence on decreasing lung function with exposure to gaseous air pollutants (NO₂, SO₂, and O₃) which can have negative effects on human health. Although according to the evidence changes in lung function are mostly linked to the exposure to environmental pollutants including CO, O₃, NO₂ and SO₂, the results should be interpreted with caution considering some following issues discussed in this review. Therefore, further studies are required considering well-designed studies in large scales to strengthen the evidence.

Impact of long-term air pollution exposure on incidence of neurodegenerative diseases: A protocol for a systematic review and exposure-response meta-analysis

BACKGROUND

Ambient air pollution is a pervasive and ubiquitous hazard, which has been linked to premature morbidity and a growing number of morbidity endpoints. Air pollution may be linked to neurodegeneration, and via this or other pathways, to neurodegenerative diseases. Emerging evidence suggests that air pollution may contribute to neurodegenerative diseases such as dementia, Parkinson's Disease (PD), Multiple Sclerosis (MS) and Motor Neuron Diseases (MND), although this evidence remains inconsistent and very limited for MS and MND. In addition, this evidence base is rapidly emerging and would benefit from a wide and critical synthesis, including a better understanding of heterogeneity.

OBJECTIVES

In this paper, we present a protocol for a systematic review and meta-analysis and specify our methods a priori. The main aim of the planned systematic review is to answer the question of whether long-term exposure (>1 year) to ambient (outdoor) air pollution (exposure, compared to lower exposure) increases the risk of adult (population) incidence of neurodegenerative diseases (outcomes) in epidemiological observational studies (study design). Another aim is to meta-analyze the associations between long-term exposure to ambient air pollutants and the risk of the selected outcomes and assess the shape of exposure-response functions. To set the stage for the proposed work, we also overview the existing epidemiological evidence in this protocol, but do not critically evaluate it, as these results will be fully presented in the planned systematic review.

SEARCH AND STUDY ELIGIBILITY

We will search the electronic databases Medline (via Ovid), Embase (via Ovid), Cochrane Library, Cinahl (via Ebscohost), Global Health (via Ebscohost), PsycINFO (via Ebscohost), Scopus, Web of Science (Core Collection), from inception to October 2022. Eligible studies must contain primary research investigating the link between 1-year + exposure to any outdoor air pollutant, from any source, and dementia, PD, MS, and MND, or dementia subtypes: Alzheimer's Disease, vascular dementia, and mixed dementia. The search strategy and eligibility criteria are pre-determined and described in full in this protocol.

STUDY APPRAISAL AND SYNTHESIS METHODS

Articles will be stored and screened using Rayyan QCRI. Title and abstract screening, full text review, data extraction, risk of bias assessment and data preparation for statistical analysis will be conducted independently by two reviewers using pre-defined forms and criteria, described in this protocol. All these steps will also be piloted and the forms and/or methods adapted if issues arise. Meta-analysis and assessment of the shape of the exposure-response functions will be conducted if four independent exposure-outcomes pairs are available, and the remainder of results will be synthesized in the forms of tables and via a narrative summary. Certainty in the body of evidence will be assessed using the OHAT approach. This protocol describes the planned analysis and synthesis a priori and serves to increase transparency and impact of this systematic review and meta-analysis.

Outpatient Department Visits and Mortality with Various Causes Attributable to Ambient Air Pollution in the Eastern Economic Corridor of Thailand

The Eastern Economic Corridor in Thailand is undergoing development, but industrial activities are causing serious air pollution. This study aimed to examine the effects of particulate matter (PM₁₀), fine particulate matter (PM_2.5), SO₂, NO₂, O₃, and CO on outpatient department (OPD) visits and mortality with various causes in the Eastern Economic Corridor, Thailand between 2013 and 2019 using a case-crossover design and conditional Poisson model. The corresponding burden of disease due to air pollution exposure was calculated. A 1 µg/m³ increase in the PM₁₀ was associated with significant increases in OPD visits for circulatory diseases (0.22, 95% CI 0.01, 0.34), respiratory diseases (0.21, 95% CI 0.13, 0.28), and skin and subcutaneous tissue diseases (0.18, 95% CI 0.10, 0.26). By contrast, a 1 µg/m³ increase in the PM₁₀ was associated with significant increases in mortality from skin and subcutaneous tissue diseases (0.79, 95% CI 0.04, 1.56). A 1 µg/m³ increase in PM_2.5 was associated with significant increases in mortality from circulatory diseases (0.75, 95% CI 0.20, 1.34), respiratory diseases (0.82, 95% CI 0.02, 1.63), and skin and subcutaneous tissue diseases (2.91, 95% CI 0.99, 4.86). The highest OPD burden was for circulatory diseases. Respiratory effects were attributed to PM₁₀ exceeding the national ambient air quality standards (NAAQS) of Thailand (120 μg/m³). The highest morbidity burden was for skin and subcutaneous tissue diseases attributed to PM_2.5 concentrations that exceeded the NAAQs (50 μg/m³). PM pollution in the EEC could strongly contribute to OPD visits and morbidity from various diseases. Preventing PM₁₀ concentrations from being higher than 60 µg/m³ could decrease OPD visits by more than 33,265 and 29,813 for circulatory and respiratory diseases, respectively. Our study suggests that such pollution increases the risks of OPD visits and mortality in various causes in the Thai EEC. Reducing the ambient air pollution concentration of NAAQSs in Thailand could reduce the health effect on the Thai population.

Ambient Air Pollution Exposure Assessments in Fertility Studies: a Systematic Review and Guide for Reproductive Epidemiologists

Purpose of Review

We reviewed the exposure assessments of ambient air pollution used in studies of fertility, fecundability, and pregnancy loss.

Recent Findings

Comprehensive literature searches were performed in the PUBMED, Web of Science, and Scopus databases. Of 168 total studies, 45 met the eligibility criteria and were included in the review. We find that 69% of fertility and pregnancy loss studies have used one-dimensional proximity models or surface monitor data, while only 35% have used the improved models, such as land-use regression models (4%), dispersion/chemical transport models (11%), or fusion models (20%). No published studies have used personal air monitors.

Summary

While air pollution exposure models have vastly improved over the past decade from a simple, one-dimensional distance or air monitor data to models that incorporate physiochemical properties leading to better predictive accuracy, precision, and increased spatiotemporal variability and resolution, the fertility literature has yet to fully incorporate these new methods. We provide descriptions of each of these air pollution exposure models and assess the strengths and limitations of each model, while summarizing the findings of the literature on ambient air pollution and fertility that apply each method.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), fine particulate matter (PM_2.5), and three speciated PM_2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO₄)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO₂ and PM_2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM_2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO₂, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species

Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, and PM2.5 constituents: SO42-, NO3-, NH4+, EC and OC) between 2005 and 2014 for the continental United States with little spatial or temporal bias. R2 ranged from 0.45 to 0.96, depending upon pollutant. Additional analysis found that temporal R2 ranged from 0.84 to 0.99 and spatial R2 values ranged from 0.76 to 0.96 across species. Four-fold cross-validation was performed to assess the model's predictive power, and ranged from 0.40 for PM10 to 0.94 for SO4 with other pollutants falling within this range. Largest improvements were found for PM10 which had substantial bias in the CMAQ fields that varied east-to-west; smallest improvements were for SO4 which was already well simulated. The random forest model results to correct the simulation biases, while largely consistent year-to-year, did show slight variation due in part to changes in the distribution of monitors and changes in CMAQ simulation inputs.

Environmental tobacco smoke and carotid intima-media thickness in healthy children and adolescents: a systematic review

Thicker carotid intima-media thickness (CIMT) has been a valid predictor for atherosclerosis development. A significant association between environmental tobacco smoke (ETS) and thickening of CIMT has been demonstrated in adults, whereas such association has scarcely been reviewed in paediatric population. The dominate electronic databases, including MEDLINE (Ovid), PubMed, Embase, CINAHL, Web of Science, Scopus, were searched from inception. Reference lists of retrieved articles were further scanned as to avoid any missing literatures. Newcastle-Ottawa scale was used to assess the quality of the included studies. Qualitative synthesis analyses were performed on the selected studies. 331 articles were retrieved, and 4 were finally selected. All four studies investigated the association between postnatal ETS and CIMT in children, and three of them reported a statistically significant positive association. Three studies investigated the association between prenatal maternal ETS and CIMT, and one of the three found a positive association. Two studies explored the association between postnatal maternal ETS and CIMT, one reported a positive association. Two studies used serum cotinine measurement to quantify ETS and demonstrated potential dose-response relationship with CIMT. ETS exposure may play an independent role in the development of cardiovascular risks in healthy children and adolescents. In the consideration of the great burden of respiratory and cardiovascular diseases, there is an urgent need of effective surveillance for paediatric population's ETS exposure to reduce smoke exposure.

Relationship Between Environmental Air Quality and Congenital Heart Defects

BACKGROUND

Congenital heart defects (CHDs) affect 40,000 U.S. infants annually. One fourth of these infants have a critical CHD, requiring intervention within the first year of life for survival. Over 80% of CHDs have an unknown etiology. Fine particulate matter ≤2.5 (PM2.5) and ozone (O3) may be air pollutants associated with CHD.

OBJECTIVES

The purpose of this study was to explore relationships between first-trimester maternal exposure to air pollutants PM2.5 and O3 and a critical CHD diagnosis.

METHODS

A retrospective cohort study with nested case controls was conducted using data from January 1, 2014, to December 31, 2016, and consisted of 199 infants with a diagnosed critical CHD and 550 controls. Air pollution data were obtained from the U.S. Environmental Protection Agency air monitors. Geographic information system software was used to geocode monitoring stations and infant residential locations. Data analysis included frequencies, chi-square, independent t-test analysis, and binary logistic regression for two time periods: the entire first trimester (Weeks 1-12) and the critical exposure window (Weeks 3-8 gestation).

RESULTS

Critical CHD odds were not significantly increased by exposure during the first trimester. However, weekly analyses revealed CHD odds were higher in Weeks 5 and 8 as PM2.5 increased and decreased in Week 11 with increased O3 exposure.

DISCUSSION

Our study shows no evidence to support the overall association between air pollutants PM2.5 and O3 and a critical CHD diagnosis. However, analyses by week suggested vulnerability in certain weeks of gestation and warrant additional surveillance and study.

PM2.5 exposure as a risk factor for type 2 diabetes mellitus in the Mexico City metropolitan area

BACKGROUND

Exposure to air pollution is the main risk factor for morbidity and mortality in the world. Exposure to particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) is associated with cardiovascular and respiratory conditions, as well as with lung cancer, and there is evidence to suggest that it is also associated with type II diabetes (DM). The Mexico City Metropolitan Area (MCMA) is home to more than 20 million people, where PM2.5 levels exceed national and international standards every day. Likewise, DM represents a growing public health problem with prevalence around 12%. In this study, the objective was to evaluate the association between exposure to PM2.5 and DM in adults living in the MCMA.

METHODS

Data from the 2006 or 2012 National Health and Nutrition Surveys (ENSANUT) were used to identify subjects with DM and year of diagnosis. We estimated PM2.5 exposure at a residence level, based on information from the air quality monitoring system (monitors), as well as satellite measurements (satellite). We analyzed the relationship through a cross-sectional approach and as a case - control study.

RESULTS

For every 10 μg/m3 increase of PM2.5 we found an OR = 3.09 (95% CI 1.17-8.15) in the 2012 sample. These results were not conclusive for the 2006 data or for the case - control approach.

CONCLUSIONS

Our results add to the evidence linking PM2.5 exposure to DM in Mexican adults. Studies in low- and middle-income countries, where PM2.5 atmospheric concentrations exceed WHO standards, are required to strengthen the evidence.

Passive environmental residential exposure to agricultural pesticides and hematological malignancies in the general population: a systematic review

Incidence rates of hematological malignancies have been constantly increasing over the past 40 years. In parallel, an expanding use of agricultural pesticides has been observed. Only a limited number of studies investigated the link between hematological malignancies risk and passive environmental residential exposure to agricultural pesticides in the general population. The purpose of our review was to summarize the current state of knowledge on that question. A systematic literature search was conducted using PubMed and Scopus databases. We built a scoring scale to appraise relevance of each selected articles. We included 23 publications: 13 ecological studies, 9 case-control studies and a cohort study. Positive associations were reported between hematological malignancies and individual pesticides, pesticide groups, all pesticides without distinction, or some crop types. Relevance score was highly various across studies regardless of their design. Children studies were the majority and had overall higher relevance scores. The effect of passive environmental residential exposure to agricultural pesticides on hematological malignancies risk is suggested by the literature. The main limitation of the literature available is the high heterogeneity across studies, especially in terms of exposure assessment approach. Further studies with high methodological relevance should be conducted.

AERMOD modeling of ambient manganese for residents living near a ferromanganese refinery in Marietta, OH, USA

Elevated exposure to ambient manganese (Mn) is associated with adverse health outcomes. In Marietta, Ohio, the primary source of ambient Mn exposure is from the longest operating ferromanganese refinery in North America. In this study, the US EPA air dispersion model, AERMOD, was used to estimate ambient air Mn levels near the refinery for the years 2008-2013. Modeled air Mn concentrations for 2009-2010 were compared to concentrations obtained from a stationary air sampler. Census block population data were used to estimate population sizes exposed to an annual average air Mn > 50 ng/m3, the US EPA guideline for chronic exposure, for each year. Associations between modeled air Mn, measured soil Mn, and measured indoor dust Mn in the modeled area were also examined. Median modeled air Mn concentrations ranged from 6.3 to 43 ng/m3 across the years. From 12,000-56,000 individuals, including over 2000 children aged 0-14 years, were exposed to respirable annual average ambient air Mn levels exceeding 50 ng/m3 in five of the six years. For 2009-2010, the median modeled air Mn concentration at the stationary site was 20 ng/m3, compared to 18 ng/m3 measured with the stationary air sampler. All model performance measures for monthly modeled concentrations compared to measured concentrations were within acceptable limits. The study shows that AERMOD modeling of ambient air Mn is a viable method for estimating exposure from refinery emissions and that the Marietta area population was at times exposed to Mn levels that exceeded US EPA guidelines.

Comparison of measured residential black carbon levels outdoors and indoors with fixed-site monitoring data and with dispersion modelling

Epidemiologic studies on health effects of air pollution usually rely on time-series of ambient monitoring data or on spatially modelled levels. Little is known how well these estimate residential outdoor and indoor levels. We investigated the agreement of measured residential black carbon (BC) levels outdoors and indoors with fixed-site monitoring data and with levels calculated using a Gaussian dispersion model. One-week residential outdoor and indoor BC measurements were conducted for 15 families living in central Stockholm. Time-series from urban background and street-level monitors were compared to these measurements. The observed weekly concentrations were also standardized to reflect annual averages, using urban background levels, and compared spatially to long-term levels as estimated by dispersion modelling. Weekly average outdoor BC level was 472 ng/m³ (range 261-797 ng/m³). The corresponding fixed-site urban background and street levels were 313 and 1039 ng/m³, respectively. Urban background variation explained 50% of the temporal variation in residential outdoor levels averaged over 24 h. Modelled residential long-term outdoor levels were on average comparable with the standardized measured home outdoor levels, and explained 49% of the spatial variability. The median indoor/outdoor ratio across all addresses was 0.79, with no difference between day and night time. Common exposure estimation approaches in the epidemiology of health effects related to BC displayed high validity for residencies in central Stockholm. Urban background monitored levels explained half of the outdoor day-to-day variability at residential addresses. Long-term dispersion modelling explained half of the spatial differences in outdoor levels. Indoor BC concentrations tended to be somewhat lower than outdoor levels.

Reported Neighborhood Traffic and the Odds of Asthma/Asthma-Like Symptoms: A Cross-Sectional Analysis of a Multi-Racial Cohort of Children

Asthma in children poses a significant clinical and public health burden. We examined the association between reported neighborhood traffic (a proxy for traffic-related air pollution) and asthma among 855 multi-racial children aged 4-8 years old who participated in the Environmental Influences on Child Health Outcomes (ECHO) cohort. We hypothesized that high neighborhood traffic density would be associated with the prevalence of asthma. Asthma/asthma-like symptoms (defined as current and/or past physician diagnosed asthma, past wheezing, or nighttime cough or wheezing in the past 12 months) was assessed by parental report. The relationship between neighborhood traffic and asthma/asthma-like symptoms was assessed using logistic regression. The prevalence of asthma/asthma-like symptoms among study participants was 23%, and 15% had high neighborhood traffic. Children with significant neighborhood traffic had a higher odds of having asthma/asthma-like symptoms than children without neighborhood traffic [adjusted OR = 2.01 (95% CI: 1.12, 3.62)] after controlling for child's race-ethnicity, age, sex, maternal education, family history of asthma, play equipment in the home environment, public parks, obesity and prescribed asthma medication. Further characterization of neighborhood traffic is needed since many children live near high traffic zones and significant racial/ethnic disparities exist.

Outdoor air pollution from industrial chemicals causing new onset of asthma or COPD: a systematic review protocol

BACKGROUND

Until today, industrial sources contribute to the multifaceted contamination of environmental air. Exposure to air pollutants has the potential to initiate and promote asthma and chronic obstructive pulmonary disease (COPD). At global scale, both entities cause the majority of about 4 million annual deaths by respiratory disease. However, we identified industrial contamination as a subgroup of air pollution that may be associated with this burden and is underinvestigated in research. Therefore, the aim of this study is to investigate associations between substances industrially released into environmental air and the occurrence of asthma and COPD in the human population. Here we present the protocol for our systematic review of the current evidence.

METHODS

The following determinations will be applied during the systematic review process and are specified in the protocol that complies with the PRISMA-P statement. Populations of children and adults, as well as outdoor workers, exposed to industrially released air pollutants are of interest. Eligible studies may include subjects as controls who are non- or less exposed to the investigated air pollutants. The outcomes new-onset asthma and/or COPD investigated with risk ratio, odds ratio, hazard ratio, incidence rate ratio, cumulative incidence, and incidence rate are eligible. We will search the electronic literature databases EMBASE, MEDLINE, and Web of Science for peer-reviewed reports of incidence studies and incidence case-control studies. After systematic sorting of initial records, included studies will be subjected to quality assessment. Data will be synthesized qualitatively and, if appropriate, quantitatively for risk ratio and odds ratio. We will maintain and provide a PRISMA report.

DISCUSSION

Results of this systematic review may indicate alterations of incidence and risk of asthma and/or COPD in populations within industrial exposure radiuses including outdoor workplaces. Specific causal substances and compositions will be identified, but results will depend on the exposure assessment of the eligible studies. Our approach covers effects of industrial contributions to overall air pollution if studies reportedly attribute investigated emissions to industry. Results of this study may raise the question wether the available higher-level evidence sufficiently covers the current scale of industrial exposure scenarios and their potential harm to respiratory health.

TRIAL REGISTRATION

This protocol was registered in PROSPERO, registration number CRD42020151573 .

Association between maternal exposure to pollutant particulate matter 2.5 and congenital heart defects: a systematic review

OBJECTIVE

The objective of this review was to determine if there was an association between maternal exposure to pollutant particulate matter 2.5 during the first trimester of pregnancy and congenital heart defects within the first year of life.

INTRODUCTION

The environment is recognized as an important determinant of health for both the individual and population. Air pollution specifically is a major environmental risk factor impacting health with links to asthma, heart disease, obesity, and fetal developmental complications. Of the commonly monitored air pollutants, particulate matter 2.5 has associations with health, especially among vulnerable populations such as children and pregnant women. A congenital heart defect is a fetal complication that impacts 34.3 million infants globally, with more than 80% of the diagnoses having an unknown etiology. Although environmental risk factors such as air pollution are thought to be a risk factor in the diagnosis of a congenital heart defect, epidemiologic research evidence is limited.

INCLUSION CRITERIA

This review considered studies that evaluated maternal exposure to the air pollutant particulate matter 2.5 during the first trimester (weeks 1-12) of fetal development. The primary outcome was a diagnosis of a congenital heart defect in an infant within the first year of life.

METHODS

A three-step search strategy was utilized in this review and included 11 databases and two websites. Studies published from January 2002 to September 2018 were eligible for inclusion. Only papers published in English were included. Eligible studies underwent critical appraisal by two independent reviewers using standardized critical appraisal instruments from JBI. Quantitative data were extracted from the included studies independently by two reviewers. Odds ratios (ORs) and 95% confidence intervals (CIs) were extracted for the individual outcome measures, specifically atrial septal defect, ventricular septal defect, and tetralogy of fallot, respectively. The defects were identified and pooled, where possible, in statistical meta-analysis. Where statistical pooling was not possible, findings were reported narratively.

RESULTS

Five studies were identified that met the inclusion criteria, including three cohort and two case-control designs. Each individual study identified at least one statistically significant increase or inverse association between particulate matter 2.5 and a congenital heart defect. An increased risk was identified with more than seven isolated and two groupings of congenital heart defects. An inverse risk was identified with two isolated and one grouping of congenital heart defects. Meta-analysis results were: atrial septal defect, OR = 0.65 (95% CI, 0.37 to 1.15); ventricular septal defect, OR = 1.02 (95% CI, 075 to 1.37); and tetralogy of fallot, OR = 1.16 (95% CI, 0.78 to 1.73), indicating no statistically significant findings.

CONCLUSION

There was no significant evidence to support an association between air pollutant particulate matter 2.5 and a congenital heart defect in the first year of life. However, few studies met the rigorous inclusion criteria, and the studies that did had high heterogeneity, making it difficult to complete a meta-analysis with such a limited number of articles. Further research is needed to standardize the outcomes and pollutant monitoring methods, and provide comparable analysis results so that future synthesis of the literature can be conducted.

Assessing schoolchildren's exposure to air pollution during the daily commute - A systematic review

Air pollution is mostly caused by emissions from human activities, and exposure to air pollution is linked with numerous adverse human health outcomes. Recent studies have identified that although people only spend a small proportion of time on their daily commutes, the commuter microenvironment is a significant contributor to their total daily air pollution exposure. Schoolchildren are a particularly vulnerable cohort of the population, and their exposure to air pollution at home or school has been documented in a number of case studies. A few studies have identified that schoolchildren's exposure during commutes is linked with adverse cognitive outcomes and severe wheeze in asthmatic children. However, the determinants of total exposure, such as route choice and commute mode, and their subsequent health impacts on schoolchildren are still not well-understood. The aim of this paper is to review and synthesize recent studies on assessing schoolchildren's exposure to various air pollutants during the daily commute. Through reviewing 31 relevant studies published between 2004 and 2020, we tried to identify consistent patterns, trends, and underlying causal factors in the results. These studies were carried out across 10 commute modes and 12 different air pollutants. Air pollution in cities is highly heterogeneous in time and space, and commuting schoolchildren move through the urban area in complex ways. Measurements from fixed monitoring stations (FMSs), personal monitoring, and air quality modeling are the three most common approaches to determining exposure to ambient air pollutant concentrations. The time-activity diary (TAD), GPS tracker, online route collection app, and GIS-based route simulation are four widely used methods to determine schoolchildren's daily commuting routes. We found that route choices exerted a determining impact on schoolchildren's exposure. It is challenging to rank commute modes in order of exposure, as each scenario has numerous uncontrollable determinants, and there are notable research gaps. We suggest that future studies should concentrate on examining exposure patterns of schoolchildren in developing countries, exposure in the subway and trains, investigating the reliability of current simulation methods, exploring the environmental justice issue, and identifying the health impacts during commuting. It is recommended that three promising tools of smartphones, data fusion, and GIS should be widely used to overcome the challenges encountered in scaling up commuter exposure studies to population scales.

Passenger comfort and ozone pollution exposure in an air-conditioned bus microenvironment

Ground-level ozone is the primary source of air pollution in China, particularly during the warmer months. In this study, we investigated the exposure status of ozone pollution and the temperature distribution in an air-conditioned bus in Jinan during the evening peak period based on field measurements obtained with a handheld portable particle counter and indigo disulfonate spectrophotometry. Statistical analysis showed that the passengers experienced poor air quality within the confines of the bus due to the poor air quality outside. Furthermore, the level of passenger comfort was dissatisfactory because of the high temperature, thereby highlighting the urgent need to improve the current situation. Numerical simulations were conducted using FLUENT software to explore the impacts of the air supply angle, the opening and closing of the bus door, and the chemical reaction between ozone and its precursors on the diffusion and distribution of ozone, the temperature, and the airflow field. The results indicated that high concentrations of ozone were present in the middle and front regions of the bus. Pollution can be reduced by keeping the bus door open for no longer than 20 s when waiting for other passengers, and the best optimization effect in relation to the temperature and passenger comfort was determined as an air supply angle of 30°. In addition, the average individual daily intake of ozone was combined with other relevant parameters to assess the exposure level. It is recommended that the elderly and children should avoid peak time travel to reduce their exposure to ozone (inhalation dose values > 60 μg/m³ and > 56 μg/m³ according to simulations, respectively). These findings are expected to effectively improve the air quality and passenger comfort levels in busses, thereby protecting the health of passengers and reducing carbon usage.

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen's health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO2 and 19% more to PM2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO2 and PM2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO2 and 15% for PM2.5). Results for population-weighted exposure revealed exposure to PM2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from -12% to +7% for NO2 and PM2.5. The developed "dynamic transport approach" is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

Population based Air Pollution Exposure and its influence factors by Integrating Air Dispersion Modeling with GIS Spatial Analysis

Air pollution is a major environmental health problem. The study of interaction between air pollution and human will benefit to the human health and well-being of community. Both a model for assessing population relative risk of air pollution exposure (MAPRRAPE) and air pollution concentration methods were applied in a case study to determine the optimal method in evaluating risk of population exposure to Sulfur Dioxide (SO₂). The framework for building the MAPRRAPE was described in detail. Then, the spatial patterns of population by demographic characteristics exposed to SO₂ from industrial, vehicle, and the mixture of industrial and vehicle pollution sources, as well as an in-depth quantitative investigation using correlation analysis were studied for further source appointment. The results showed that the MAPRRAPE was more reliable than air pollution concentration model in determining population exposure risks by demographic characteristics. The high risk areas of whites exposed to SO₂ were larger than blacks and the other races due to a large number of whites, and other age groups exposed to SO₂ were larger than children and the old people. In addition, the correlation analyses showed that the relative risks of population by demographic characteristics exposed to SO₂ had a more significant correlation with vehicle pollution source than industrial pollution source. The results of source appointment thus demonstrated that vehicle pollution source was the main pollution source. This study suggests that there is a clear need for the implementation of programs and services that will reduce population exposed to air pollution with focusing on densely populated areas for an ultimate improvement of community health status and the environmental conditions.

Particulate Matter Exposure and Attention-Deficit/Hyperactivity Disorder in Children: A Systematic Review of Epidemiological Studies

Attention-deficit/hyperactivity disorder (ADHD) is the most common cognitive and behavioural disorder affecting children, with a worldwide-pooled prevalence of around 5%. Exposure to particulate matter (PM) air pollution is suspected to be associated with autism spectrum disorders and recent studies have investigated the relationship between PM exposure and ADHD. In the absence of any synthesis of the relevant literature on this topic, this systematic review of epidemiological studies aimed to investigate the relationship between the exposure of children to PM and ADHD and identify gaps in our current knowledge. In December 2018, we searched the PubMed and EMBASE databases. We only included epidemiological studies carried out on children without any age limit, measuring PM exposure and health outcomes related to ADHD. We assessed the quality of the articles and the risk of bias for each included article using the Newcastle-Ottawa Scale and the Office of Health Assessment and Translation (OHAT) approach, respectively. The keyword search yielded 774 results. Twelve studies with a total number of 181,144 children met our inclusion criteria, of which 10 were prospective cohort studies and 2 were cross-sectional studies. We subsequently classified the selected articles as high or good quality studies. A total of 9 out of the 12 studies reported a positive association between PM exposure to outdoor air pollution and behavioral problems related to attention. Despite these results, we found a significant degree of heterogeneity among the study designs. Furthermore, 11 studies were judged to be at a probably high risk of bias in the exposure assessment. In conclusion, we opine that further high quality studies are still needed in order to clarify the association between PM exposure and ADHD diagnosis.

Activity-based air pollution exposure assessment: Differences between homemakers and cycling commuters

Long-term air pollution exposure may lead to an increase in incidences and mortality rates of chronic diseases and adversely affect human health. The effects of long-term air pollution exposure have not been comprehensively studied due to the lack of human mobility data collected over a long period. In this study, we develop and apply a personal mobility model to long-term hourly air pollution concentration predictions to quantify personal long-term air pollution exposure for all individuals. We implement our model assuming mobility patterns for commuters and homemakers, and separate between weekdays and weekend. Our results show that NO₂ exposure of commuters are on average slightly higher and vary less spatially as they are exposed to NO₂ at multiple locations.

Fine particulate matter is a potential determinant of Alzheimer's disease: A systemic review and meta-analysis

Air pollution is a modifiable and preventable factor, and it is a possible risk factor for dementia. However, evidence from epidemiological studies is still limited. We conducted a systematic review and meta-analysis to summarize the epidemiological evidence for long-term effects of particulate matter with an aerodynamic diameter ≤2.5 μm (PM_2.5) on dementia/Alzheimer's disease (AD). Our inclusion criteria for eligible studies were: longitudinal cohort study design, no overlap in study population, age of study subject ≥50 years, detailed description of exposure assessment for PM_2.5, outdoor assessment of exposure to PM_2.5, usage of a clear definition of dementia/AD, and accessibility of sufficient information for meta-analysis. Six databases were searched for eligible studies. The random-effect model was used to synthesize the associations between PM_2.5 and dementia. After exclusion of all irrelevant studies, we analyzed the results of four cohort studies conducted in Canada, Taiwan, the UK, and the US during 2015-2018 among more than 12 million elderly subjects aged ≥50 years (N = 12,119,853). Our meta-analysis reveals that exposure to a 10 μg/m³ increase in PM_2.5 was significantly and positively associated with dementia (pooled HR = 3.26, 95% CI: 1.20, 5.31). In subgroup analyses, exposure to a 10 μg/m³ increase in PM_2.5 was found to be positively associated with AD (pooled HR = 4.82, 95% CI: 2.28, 7.36). Analysis of current epidemiological research on PM_2.5 and dementia confirmed that exposure to PM_2.5 was positively associated with a higher risk for dementia. However, it is to be noted that the included studies mainly relied on claim-based diagnosis and showed large differences in methods of exposure assessment, hence further epidemiological studies with well validated outcomes and with standardized exposure assessment models are required to ascertain the relationship between PM_2.5 and dementia/AD.

Characteristics of cohort studies of long-term exposure to PM2.5: a systematic review

This study systematically reviewed all the cohort studies investigating the relationship between long-term exposure to PM_2.5 and any health outcome until February 2018. We searched ISI Web of Knowledge, Pubmed, and Scopus databases for peer-reviewed journal research articles published in English. We only extracted the results of the single-pollutant main analysis of each study, excluding the effect modifications and sensitivity analyses. Out of the initial 9523 articles, 203 articles were ultimately included for analysis. Based on the different characteristics of studies such as study design, outcome, exposure assessment method, and statistical model, we calculated the number and relative frequency of analyses with statistically significant and insignificant results. Most of the studies were prospective (84.8%), assessed both genders (66.5%), and focused on a specific age range (86.8%). Most of the articles (78.1%) had used modeling techniques for exposure assessment of cohorts' participants. Among the total of 317 health outcomes, the most investigated outcomes include mortality due to cardiovascular disease (6.19%), all causes (5.48%), lung cancer (4.00%), ischemic heart disease (3.50%), and non-accidental causes (3.50%). The percentage of analyses with statistically significant results were higher among studies that used prospective design, mortality as the outcome, fixed stations as exposure assessment method, hazard ratio as risk measure, and no covariate adjustment. We can somehow conclude that the choice of right characteristics for cohort studies can make a difference in their results.

Assessing Agreement in Exposure Classification between Proximity-Based Metrics and Air Monitoring Data in Epidemiology Studies of Unconventional Resource Development

Recent studies of unconventional resource development (URD) and adverse health effects have been limited by distance-based exposure surrogates. Our study compared exposure classifications between air pollutant concentrations and “well activity” (WA) metrics, which are distance-based exposure proxies used in Marcellus-area studies to reflect variation in time and space of residential URD activity. We compiled Pennsylvania air monitoring data for benzene, carbon monoxide, nitrogen dioxide, ozone, fine particulates and sulfur dioxide, and combined this with data on nearly 9000 Pennsylvania wells. We replicated WA calculations using geo-coordinates of monitors to represent residences and compared exposure categories from air measurements and WA at the site of each monitor. There was little agreement between the two methods for the pollutants included in the analysis, with most weighted kappa coefficients between −0.1 and 0.1. The exposure categories agreed for about 25% of the observations and assigned inverse categories 16%–29% of the time, depending on the pollutant. Our results indicate that WA measures did not adequately distinguish categories of air pollutant exposures and employing them in epidemiology studies can result in misclassification of exposure. This underscores the need for more robust exposure assessment in future analyses and cautious interpretation of these existing studies.

Assessment of Traffic-Related Air Pollution: Case Study of Pregnant Women in South Texas

Population groups vulnerable to adverse effects of traffic-related air pollution correspond to children, pregnant women and elderly. Despite these effects, literature is limited in terms of studies focusing on these groups and a reason often cited is the limited information on their mobility important for exposure assessment. The current study presents a method for assessing individual-level exposure to traffic-related air pollution by integrating mobility patterns tracked by global positioning system (GPS) devices with dynamics of air pollutant concentrations. The study is based on a pool of 17 pregnant women residing in Hidalgo County, Texas. The traffic-related particulate matter with diameter of less than 2.5 micrometer (PM_2.5) emissions and air pollutant concentrations are predicted using MOVES and AERMOD models, respectively. The daily average traffic-related PM_2.5 concentration was found to be 0.32 µg/m³, with the highest concentration observed in transit (0.56 µg/m³), followed by indoors (0.29 µg/m³), and outdoor (0.26 µg/m³) microenvironment. The obtained exposure levels exhibited considerable variation between time periods, with higher levels during peak commuting periods, close to the US–Mexico border region and lower levels observed during midday periods. The study also assessed if there is any difference between traffic-related dynamic exposure, based on time-varying mobility patterns, and static exposure, based solely on residential locations, and found a difference of 9%, which could be attributed to the participants’ activity patterns being focused mostly indoors.

Association between ambient gaseous and particulate air pollutants and attention deficit hyperactivity disorder (ADHD) in children; a systematic review

ADHD is a common neurodevelopmental disorder highly attributed to genetics, but the combination of other social and environmental determinants, as well as potential gene-environment interactions, can also be responsible. This paper aims to review relevant literature published up to April 2018 for determining whether air pollution caused by ambient gaseous (NO₂, SO₂, PCDD/Fs, Benzene) and particulate matters (PM₁₀, PM_2.5, PM₇, PAH, BC/EC) as an environmental risk factor is associated with increased risk of ADHD in children. Relevant literature was identified through electronic searches of PubMed, Embase, Web of Science, Scopus database and gray literature. A total of 872 articles were initially identified 28 of which meeting the defined inclusion criteria were included. The methodological quality of the included articles was evaluated using the modified Critical Appraisal Skills Programs (CASP) and confounding variables, exposure and outcome measurement were assessed. The results of this systematic review revealed that there is more evidence on the detrimental effects of EC, BC, and PM on ADHD compared to PAH. Among gaseous air pollutants, association was found between SO₂ and urinary level of t,t-MA (trans, trans-muconic acid) as a proxy-biomarker of NO₂ exposure, not merely benzene. However few studies related to NO₂ (0.46%) found detrimental effects. Overall, the number of studies reporting an association between air pollution and increased risk of ADHD is relatively higher compared to the number of studies reporting no association. However, the findings of the studies provided limited evidence to support the idea that exposure to air pollution may be linked to increased risk of ADHD. Well-designed and harmonized studies considering standard methods for individual exposure assessment, critical windows of susceptibility, and appropriate tools for outcome measurement, can improve the quality of epidemiological studies and strengthen the evidence. Since ADHD with its long-term consequences can impose large costs to communities and impact the children performance, determination of the risk factors in children and particularly the role of the environment as priorities for research should be considered.

A Time-Based Objective Measure of Exposure to the Food Environment

Exposure to food environments has mainly been limited to counting food outlets near participants’ homes. This study considers food environment exposures in time and space using global positioning systems (GPS) records and fast food restaurants (FFRs) as the environment of interest. Data came from 412 participants (median participant age of 45) in the Seattle Obesity Study II who completed a survey, wore GPS receivers, and filled out travel logs for seven days. FFR locations were obtained from Public Health Seattle King County and geocoded. Exposure was conceptualized as contact between stressors (FFRs) and receptors (participants’ mobility records from GPS data) using four proximities: 21 m, 100 m, 500 m, and ½ mile. Measures included count of proximal FFRs, time duration in proximity to ≥1 FFR, and time duration in proximity to FFRs weighted by FFR counts. Self-reported exposures (FFR visits) were excluded from these measures. Logistic regressions tested associations between one or more reported FFR visits and the three exposure measures at the four proximities. Time spent in proximity to an FFR was associated with significantly higher odds of FFR visits at all proximities. Weighted duration also showed positive associations with FFR visits at 21-m and 100-m proximities. FFR counts were not associated with FFR visits. Duration of exposure helps measure the relationship between the food environment, mobility patterns, and health behaviors. The stronger associations between exposure and outcome found at closer proximities (<100 m) need further research.

A systematic literature review and critical appraisal of epidemiological studies on outdoor air pollution and tuberculosis outcomes

Ambient air pollution is the leading environmental risk factor for disease globally. Air pollutants can increase the risk of some respiratory infections, but their effects on tuberculosis (TB) are unclear. In this systematic literature review, we aimed to assess epidemiological studies on the association between outdoor air pollutants and TB incidence, hospital admissions and death (collectively referred to here as 'TB outcomes'). We sought to consolidate available evidence on this topic and propose recommendations for future studies. Following PRISMA guidelines, we searched PubMed, Web of Science, Google Scholar, and Scopus with no restrictions imposed on year of publication. A total of 11 epidemiological studies, performed in Asia, Europe and North America, met our inclusion criteria (combined sample size: 215,337 people). We extracted key study characteristics from each eligible publication, including design, exposure assessment, analytical approaches and effect estimates. The studies were assessed for overall quality and risk of bias using standard criteria. The pollutant most frequently associated with statistically significant effects on TB outcomes was fine particulate matter ( < 2.5 µm; PM_2.5); 6/11 studies assessed PM_2.5, of which 4/6 demonstrated a significant association). There was some evidence of significant associations between PM₁₀ ( < 10 µm), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) and TB outcomes, but these associations were inconsistent. The existing epidemiological evidence is limited and shows mixed results. However, it is plausible that exposure to air pollutants, particularly PM_2.5, may suppress important immune defence mechanisms, increasing an individual's susceptibility to development of active TB and TB-related mortality. Considering the small number of studies relative to the demonstrably large global health burdens of air pollution and TB, further research is required to corroborate the findings in the current literature. Based on a critical assessment of existing evidence, we conclude with methodological suggestions for future studies.

Current State of Research on the Risk of Morbidity and Mortality Associated with Air Pollution in Korea

PURPOSE

The effects of air pollution on health can vary regionally. Our goal was to comprehensively review previous epidemiological studies on air pollution and health conducted in Korea to identify future areas of potential study.

MATERIALS AND METHODS

We systematically searched all published epidemiologic studies examining the association between air pollution and occurrence of death, diseases, or symptoms in Korea. After classifying health outcomes into mortality, morbidity, and health impact, we summarized the relationship between individual air pollutants and health outcomes.

RESULTS

We analyzed a total of 27 studies that provided 104 estimates of the quantitative association between risk of mortality and exposure to air pollutants, including particulate matter with aerodynamic diameter less than 10 μm, particulate matter with aerodynamic diameter less than 2.5 μm, sulfur dioxide, nitrogen dioxide, ozone, and carbon monoxide in Korea between January 1999 and July 2018. Regarding the association with morbidity, there were 38 studies, with 98 estimates, conducted during the same period. Most studies examined the short-term effects of air pollution using a time series or case-crossover study design; only three cohort studies that examined long-term effects were found. There were four health impact studies that calculated the attributable number of deaths or disability-adjusted life years due to air pollution.

CONCLUSION

There have been many epidemiologic studies in Korea regarding air pollution and health. However, the present review shows that additional studies, especially cohort and experimental studies, are needed to provide more robust and accurate evidence that can be used to promote evidence-based policymaking.

Development and performance evaluation of a GIS-based metric to assess exposure to airborne pollutant emissions from industrial sources

BACKGROUND

Dioxins are environmental and persistent organic carcinogens with endocrine disrupting properties. A positive association with several cancers, including risk of breast cancer has been suggested.

OBJECTIVES

This study aimed to develop and assess performances of an exposure metric based on a Geographic Information System (GIS) through comparison with a validated dispersion model to estimate historical industrial dioxin exposure for its use in a case-control study nested within a prospective cohort.

METHODS

Industrial dioxin sources were inventoried over the whole French territory (n > 2500) and annual average releases were estimated between 1990 and 2008. In three selected areas (rural, urban and urban-costal), dioxin dispersion was modelled using SIRANE, an urban Gaussian model and exposure of the French E3N cohort participants was estimated. The GIS-based metric was developed, calibrated and compared to SIRANE results using a set of parameters (local meteorological data, characteristics of industrial sources, e.g. emission intensity and stack height), by calculating weighted kappa statistics (wκ) and coefficient of determination (R2). Furthermore, as performance evaluation, the final GIS-based metric was tested to assess atmospheric exposure to cadmium.

RESULTS

The concordance between the GIS-based metric and the dispersion model for dioxin exposure estimate was strong (wκ median = 0.78 (1st quintile = 0.72, 3rd quintile =0.82) and R2 median = 0.82 (1st quintile = 0.71, 3rd quintile = 0.87)). We observed similar performance for cadmium.

CONCLUSIONS

Our study demonstrated the ability of the GIS-based metric to reliably characterize long-term environmental dioxin and cadmium exposures as well as the pertinence of using dispersion modelling to construct and calibrate the GIS-based metric.

Associations between maternal residential proximity to air emissions from industrial facilities and low birth weight in Texas, USA

BACKGROUND

Most previous studies examining associations between maternal exposures to air pollutants during pregnancy and low birth weight (LBW) in offspring focused on criteria air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO, and Pb). The relationship between non-criteria air pollutants and LBW is understudied and requires greater coverage.

OBJECTIVES

This study investigated associations between maternal residential exposure to industrial air pollutants during pregnancy and LBW in offspring.

METHODS

This study used a case-control study design that included 94,106 term LBW cases and 376,424 controls. It covered 78 air pollutants common to both the Toxics Release Inventory (TRI) and ground air quality monitoring databases in Texas during 1996-2008. A modified version of the Emission Weighted Proximity Model (EWPM), calibrated with ground monitoring data, was used to estimate maternal residential exposure to industrial air pollutants during pregnancy. Binary logistic regression analyses were performed to calculate odds ratios (ORs) reflecting the associations of maternal exposure to industrial air pollutants and LBW in offspring, adjusted for child's sex, gestational weeks, maternal age, education, race/ethnicity, marital status, prenatal care, tobacco use during pregnancy, public health region of maternal residence, and year of birth. In addition, the Bonferroni correction for multiple comparisons was applied to the results of logistic regression analysis.

RESULTS

Relative to the non-exposed reference group, maternal residential exposure to benzene (adjusted odds ratio (aOR) 1.06, 95% confidence interval (CI) 1.04, 1.08), benzo(g,h,i)perylene (aOR 1.04, 95% CI 1.02, 1.07), cumene (aOR 1.05, 95% CI 1.03, 1.07), cyclohexane (aOR 1.04, 95% CI 1.02, 1.07), dichloromethane (aOR 1.04, 95% CI 1.03, 1.07), ethylbenzene (aOR 1.05, 95% CI 1.03, 1.06), ethylene (aOR 1.06, 95% CI 1.03, 1.09), mercury (aOR 1.04, 95% CI 1.02, 1.07), naphthalene (aOR 1.03, 95% CI 1.01, 1.05), n-hexane (aOR 1.06, 95% CI 1.04, 1.08), propylene (aOR 1.06, 95% CI 1.03, 1.10), styrene (aOR 1.06, 95% CI 1.04, 1.08), toluene (aOR 1.05, 95% CI 1.03, 1.07), and zinc (fume or dust) (aOR 1.10, 95% CI 1.06, 1.13) was found to have significantly higher odds of LBW in offspring. When the estimated exposures were categorized into four different groups (zero, low, medium, and high) in the analysis, eleven of the fourteen air pollutants, with the exception of benzo(g,h,i)perylene, ethylene, and propylene, remained as significant risk factors.

CONCLUSIONS

Results indicate that maternal residential proximity to industrial facilities emitting any of the fourteen pollutants identified by this study during pregnancy may be associated with LBW in offspring. With the exception of benzene, ethylbenzene, toluene, and zinc, the rest of the fourteen air pollutants are identified as LBW risk factors for the first time by this study. Further epidemiological, biological, and toxicological studies are suggested to verify the findings from this study.

Industrial air pollution and low birth weight: a case-control study in Texas, USA

Many studies have investigated associations between maternal residential exposures to air pollutants and low birth weight (LBW) in offspring. However, most studies focused on the criteria air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO, and Pb), and only a few studies examined the potential impact of other air pollutants on LBW. This study investigated associations between maternal residential exposure to industrial air emissions of 449 toxics release inventory (TRI) chemicals and LBW in offspring using a case-control study design based on a large dataset consisting of 94,106 LBW cases and 376,424 controls in Texas from 1996 to 2008. Maternal residential exposure to chemicals was estimated using a modified version of the emission-weighted proximity model (EWPM). The model takes into account reported quantities of annual air emission from industrial facilities and the distances between the locations of industrial facilities and maternal residence locations. Binary logistic regression was used to compute odds ratios measuring the association between maternal exposure to different TRI chemicals and LBW in offspring. Odds ratios were adjusted for child's sex, birth year, gestational length, maternal age, education, race/ethnicity, and public health region of maternal residence. Among the ten chemicals selected for a complete analysis, maternal residential exposures to five TRI chemicals were positively associated with LBW in offspring. These five chemicals include acetamide (adjusted odds ratio [aOR] 2.29, 95% confidence interval [CI] 1.24, 4.20), p-phenylenediamine (aOR 1.63, 95% CI 1.18, 2.25), 2,2-dichloro-1,1,1-trifluoroethane (aOR 1.41, 95% CI 1.20, 1.66), tributyltin methacrylate (aOR 1.20, 95% CI 1.06, 1.36), and 1,1,1-trichloroethane (aOR 1.11, 95% CI 1.03, 1.20). These findings suggest that maternal residential proximity to industrial air emissions of some chemicals during pregnancy may be associated with LBW in offspring.

Frequency of heavy vehicle traffic and association with DNA methylation at age 18 years in a subset of the Isle of Wight birth cohort

Assessment of changes in DNA methylation (DNA-m) has the potential to identify adverse environmental exposures. To examine DNA-m among a subset of participants (n = 369) in the Isle of Wight birth cohort who reported variable near resident traffic frequencies. We used self-reported frequencies of heavy vehicles passing by the homes of study subjects as a proxy measure for TRAP, which were: never, seldom, 10 per day, 1-9 per hour and >10 per hour. Methylation of cytosine-phosphate-guanine (CpG) dinucleotide sequences in the DNA was assessed from blood samples collected at age 18 years (n = 369) in the F1 generation. We conducted an epigenome wide association study to examine CpGs related to the frequency of heavy vehicles passing by subjects' homes, and employed multiple linear regression models to assess potential associations. We repeated some of these analysis in the F2 generation (n = 140). Thirty-five CpG sites were associated with heavy vehicular traffic. After adjusting for confounders, we found 23 CpGs that were more methylated, and 11 CpGs that were less methylated with increasing heavy vehicular traffic frequency among all subjects. In the F2 generation, 2 of 31 CpGs were associated with traffic frequencies and the direction of the effect was the same as in the F1 subset while differential methylation of 7 of 31 CpG sites correlated with gene expression. Our findings reveal differences in DNA-m in participants who reported higher heavy vehicular traffic frequencies when compared to participants who reported lower frequencies.

Early life stress, air pollution, inflammation, and disease: An integrative review and immunologic model of social-environmental adversity and lifespan health

Socially disadvantaged individuals are at greater risk for simultaneously being exposed to adverse social and environmental conditions. Although the mechanisms underlying joint effects remain unclear, one hypothesis is that toxic social and environmental exposures have synergistic effects on inflammatory processes that underlie the development of chronic diseases, including cardiovascular disease, diabetes, depression, and certain types of cancer. In the present review, we examine how exposure to two risk factors that commonly occur with social disadvantage-early life stress and air pollution-affect health. Specifically, we identify neuroimmunologic pathways that could link early life stress, inflammation, air pollution, and poor health, and use this information to propose an integrated, multi-level model that describes how these factors may interact and cause health disparity across individuals based on social disadvantage. This model highlights the importance of interdisciplinary research considering multiple exposures across domains and the potential for synergistic, cross-domain effects on health, and may help identify factors that could potentially be targeted to reduce disease risk and improve lifespan health.

The association between maternal exposure to pollutant particulate matter 2.5 and neonatal congenital heart defects: a systematic review protocol

REVIEW QUESTION

The objective of this review is to identify if there is an association between maternal exposure to pollutant particulate matter 2.5 during the first trimester of pregnancy and neonatal congenital heart defects within the first year of life.

Design and application of a web-based real-time personal PM2.5 exposure monitoring system

Growing demand from public health research for conduct large-scale epidemiological studies to explore health effect of PM_2.5 was well-documented. To address this need, we design a web-based real-time personal PM_2.5 exposure monitoring system (RPPM2.5 system) which can help researcher to get big data of personal PM_2.5 exposure with low-cost, low labor requirement, and low operating technical requirements. RPPM2.5 system can provide relative accurate real-time personal exposure data for individuals, researches, and decision maker. And this system has been used in a survey of PM_2.5 personal exposure level conducted in 5 cities of China and has provided mass of valuable data for epidemiological research.

Spatial and Temporal Dynamics in Air Pollution Exposure Assessment

Analyzing individual exposure in urban areas offers several challenges where both the individual's activities and air pollution levels demonstrate a large degree of spatial and temporal dynamics. This review article discusses the concepts, key elements, current developments in assessing personal exposure to urban air pollution (seventy-two studies reviewed) and respective advantages and disadvantages. A new conceptual structure to organize personal exposure assessment methods is proposed according to two classification criteria: (i) spatial-temporal variations of individuals' activities (point-fixed or trajectory based) and (ii) characterization of air quality (variable or uniform). This review suggests that the spatial and temporal variability of urban air pollution levels in combination with indoor exposures and individual's time-activity patterns are key elements of personal exposure assessment. In the literature review, the majority of revised studies (44 studies) indicate that the trajectory based with variable air quality approach provides a promising framework for tackling the important question of inter- and intra-variability of individual exposure. However, future quantitative comparison between the different approaches should be performed, and the selection of the most appropriate approach for exposure quantification should take into account the purpose of the health study. This review provides a structured basis for the intercomparing of different methodologies and to make their advantages and limitations more transparent in addressing specific research objectives.

Carbon load in airway macrophages as a biomarker of exposure to particulate air pollution; a longitudinal study of an international Panel

Background

Carbon load in airway macrophages (AM) has been proposed as an internal marker to assess long-term exposure to combustion-derived pollutant particles. However, it is not known how this biomarker is affected by changes in exposure. We studied the clearance kinetics of black carbon (BC) in AM, obtained by sputum induction, in a one-year panel study.

Methods

AM BC was measured 8 times with 6 weeks intervals in healthy young subjects: 15 long-term residents in Leuven, Belgium (BE, mean annual PM₁₀ 20–30 μg/m³) and 30 newcomers having arrived recently (< 3 weeks) in Leuven from highly polluted cities (mean annual PM₁₀ > 50 μg/m³) in low and middle-income countries (LMIC, n = 15), or from low to moderately polluted cities in high-income countries (HIC, n = 15). The median and 90th percentile values of AM BC were quantified by image analysis of 25 macrophages per sputum sample; the carbonaceous nature of the black inclusions in AM was verified by Femtosecond Pulsed Laser Microscopy in 30 macrophages. We used a Bayesian hierarchical single-exponential decay model to describe the evolution of AM BC.

Results

In the LMIC group, the mean (95% credible interval) initial quantity (R₀) of median AM BC [1.122 (0.750–1.509) μm²] was higher than in the HIC group [0.387 (0.168–0.613) μm²] and BE group [0.275 (0.147–0.404) μm²]. Median AM BC content decreased in the LMIC group (decay constant 0.013 μm²/day), but remained stable over one year in the other two groups. In the LMIC group, clearance half-lives of 53 (30–99) and 116 (63–231) days, were calculated for median and 90th percentile AM BC, respectively.

Conclusions

In this real-life study of an international panel of healthy young subjects, we demonstrated that carbon load in airway macrophages obtained by induced sputum reflects past long-term exposure to particulate air pollution. Values of AM BC do not change over one year when exposure remains stable, but AM BC decreases upon moving from high to moderate exposure, with average half-lives of 53 and 116 days depending on the carbon load.

Electronic supplementary material

The online version of this article (10.1186/s12989-018-0250-8) contains supplementary material, which is available to authorized users.

The Human Exposure Potential from Propylene Releases to the Environment

A detailed literature search was performed to assess the sources, magnitudes and extent of human inhalation exposure to propylene. Exposure evaluations were performed at both the community and occupational levels for those living or working in different environments. The results revealed a multitude of pyrogenic, biogenic and anthropogenic emission sources. Pyrogenic sources, including biomass burning and fossil fuel combustion, appear to be the primary contributors to atmospheric propylene. Despite a very short atmospheric lifetime, measurable levels could be detected in highly remote locations as a result of biogenic release. The indoor/outdoor ratio for propylene has been shown to range from about 2 to 3 in non-smoking homes, which indicates that residential sources may be the largest contributor to the overall exposure for those not occupationally exposed. In homes where smoking takes place, the levels may be up to thirty times higher than non-smoking residences. Atmospheric levels in most rural regions are typically below 2 ppbv, whereas the values in urban levels are much more variable ranging as high as 10 ppbv. Somewhat elevated propylene exposures may also occur in the workplace; especially for firefighters or refinery plant operators who may encounter levels up to about 10 ppmv.

Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research

PURPOSE OF REVIEW

We present a review of emerging technologies and how these can transform personal air pollution exposure assessment and subsequent health research.

RECENT FINDINGS

Estimating personal air pollution exposures is currently split broadly into methods for modeling exposures for large populations versus measuring exposures for small populations. Air pollution sensors, smartphones, and air pollution models capitalizing on big/new data sources offer tremendous opportunity for unifying these approaches and improving long-term personal exposure prediction at scales needed for population-based research. A multi-disciplinary approach is needed to combine these technologies to not only estimate personal exposures for epidemiological research but also determine drivers of these exposures and new prevention opportunities. While available technologies can revolutionize air pollution exposure research, ethical, privacy, logistical, and data science challenges must be met before widespread implementations occur. Available technologies and related advances in data science can improve long-term personal air pollution exposure estimates at scales needed for population-based research. This will advance our ability to evaluate the impacts of air pollution on human health and develop effective prevention strategies.

Development of land use regression models for PM2.5, SO2, NO2 and O3 in Nanjing, China

Ambient air pollution has been a global problem, especially in China. Comparing with other methods, Land Use Regression (LUR) models can obtain air pollutant concentration distribution at finer scale without the air pollution source data based on a few monitoring sites and predictors. However, limited LUR studies have been conducted on the basis of regular monitoring networks. Thus, we explored the applicability of conducting LUR models for four key air pollutants: PM_2.5, SO₂, NO₂ and O₃, on the basis of national monitoring networks which have good representation of areas with different characteristics in Nanjing, China. Fifty-nine potential predictor variables were considered, including land use type, population density, traffic emission, industrial emission, geographical coordinates, meteorology and topography. LUR models of these four air pollutants were with good explained variance for four key air pollutants. Adjusted explained variance of the LUR models was highest for NO₂ (87%), followed by SO₂ (83%), and was lower for PM_2.5 (72%) and O₃ (65%). Annual average distributions of pollutants in 2013 were obtained based on predicted values, which revealed that O₃ in Nanjing was more heavily impacted by regional influences. This study would not only contribute to the wider use of LUR studies in China but also offer important reference for the application of regular monitoring network with high representativeness in LUR studies. These results would also support for air epidemiological studies in the future.