Modelling Europe-wide fine resolution daily ambient temperature for 2003-2020 using machine learning

To improve our understanding of the health impacts of high and low temperatures, epidemiological studies require spatiotemporally resolved ambient temperature (Ta) surfaces. Exposure assessment over various European cities for multi-cohort studies requires high resolution and harmonized exposures over larger spatiotemporal extents. Our aim was to develop daily mean, minimum and maximum ambient temperature surfaces with a 1 × 1 km resolution for Europe for the 2003-2020 period. We used a two-stage random forest modelling approach. Random forest was used to (1) impute missing satellite derived Land Surface Temperature (LST) using vegetation and weather variables and to (2) use the gap-filled LST together with land use and meteorological variables to model spatial and temporal variation in Ta measured at weather stations. To assess performance, we validated these models using random and block validation. In addition to global performance, and to assess model stability, we reported model performance at a higher granularity (local). Globally, our models explained on average more than 81 % and 93 % of the variability in the block validation sets for LST and Ta respectively. Average RMSE was 1.3, 1.9 and 1.7 °C for mean, min and max ambient temperature respectively, indicating a generally good performance. For Ta models, local performance was stable across most of the spatiotemporal extent, but showed lower performance in areas with low observation density. Overall, model stability and performance were lower when using block validation compared to random validation. The presented models will facilitate harmonized high-resolution exposure assignment for multi-cohort studies at a European scale.

Long-term exposure to air pollution and incidence of gastric and the upper aerodigestive tract cancers in a pooled European cohort: The ELAPSE project

Air pollution has been shown to significantly impact human health including cancer. Gastric and upper aerodigestive tract (UADT) cancers are common and increased risk has been associated with smoking and occupational exposures. However, the association with air pollution remains unclear. We pooled European subcohorts (N = 287,576 participants for gastric and N = 297,406 for UADT analyses) and investigated the association between residential exposure to fine particles (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone in the warm season (O3w) with gastric and UADT cancer. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level. During 5,305,133 and 5,434,843 person-years, 872 gastric and 1139 UADT incident cancer cases were observed, respectively. For gastric cancer, we found no association with PM2.5, NO2 and BC while for UADT the hazard ratios (95% confidence interval) were 1.15 (95% CI: 1.00-1.33) per 5 μg/m3 increase in PM2.5, 1.19 (1.08-1.30) per 10 μg/m3 increase in NO2, 1.14 (1.04-1.26) per 0.5 × 10-5 m-1 increase in BC and 0.81 (0.72-0.92) per 10 μg/m3 increase in O3w. We found no association between long-term ambient air pollution exposure and incidence of gastric cancer, while for long-term exposure to PM2.5, NO2 and BC increased incidence of UADT cancer was observed.

Long-term exposure to air pollution and chronic kidney disease-associated mortality - results from the pooled cohort of the European multicentre ELAPSE-study

Despite the known link between air pollution and cause-specific mortality, its relation to chronic kidney disease (CKD)-associated mortality is understudied. Therefore, we investigated the association between long-term exposure to air pollution and CKD-related mortality in a large multicentre population-based European cohort. Cohort data were linked to local mortality registry data. CKD-death was defined as ICD10 codes N18-N19 or corresponding ICD9 codes. Mean annual exposure at participant's home address was determined with fine spatial resolution exposure models for nitrogen dioxide (NO2), black carbon (BC), ozone (O3), particulate matter ≤2.5μm (PM2.5) and several elemental constituents of PM2.5. Cox regression models were adjusted for age, sex, cohort, calendar year of recruitment, smoking status, marital status, employment status and neighbourhood mean income. Over a mean follow-up time of 20.4 years, 313 of 289 564 persons died from CKD. Associations were positive for PM2.5 (hazard ratio (HR) with 95% confidence interval (CI) of 1.31 (1.03-1.66) per 5μg/m3, BC (1.26 (1.03-1.53) per 0.5×10- 5/m), NO2 (1.13 (0.93-1.38) per 10μg/m3) and inverse for O3 (0.71 (0.54-0.93) per 10μg/m3). Results were robust to further covariate adjustment. Exclusion of the largest sub-cohort contributing 226 cases, led to null associations. Among the elemental constituents, Cu, Fe, K, Ni, S and Zn, representing different sources including traffic, biomass and oil burning and secondary pollutants, were associated with CKD-related mortality. In conclusion, our results suggest an association between air pollution from different sources and CKD-related mortality.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

Perinatal exposure to traffic related air pollutants and the risk of infection in the first six months of life: a cohort study from a low-middle income country

OBJECTIVE

There is limited study from low-and-middle income countries on the effect of perinatal exposure to air pollution and the risk of infection in infant. We assessed the association between perinatal exposure to traffic related air pollution and the risk of infection in infant during their first six months of life.

METHODS

A prospective cohort study was performed in Jakarta, March 2016-September 2020 among 298 mother-infant pairs. PM2.5, soot, NOx, and NO2 concentrations were assessed using land use regression models (LUR) at individual level. Repeated interviewer-administered questionnaires were used to obtain data on infection at 1, 2, 4 and 6 months of age. The infections were categorized as upper respiratory tract (runny nose, cough, wheezing or shortness of breath), lower respiratory tract (pneumonia, bronchiolitis) or gastrointestinal tract infection. Logistic regression models adjusted for covariates were used to assess the association between perinatal exposure to air pollution and the risk of infection in the first six months of life.

RESULTS

The average concentrations of PM2.5 and NO2 were much higher than the WHO recommended levels. Upper respiratory tract infections (URTI) were much more common in the first six months of life than diagnosed lower respiratory tract or gastro-intestinal infections (35.6%, 3.5% and 5.8% respectively). Perinatal exposure to PM2.5 and soot suggested increase cumulative risk of upper respiratory tract infection (URTI) in the first 6 months of life per IQR increase with adjusted OR of 1.50 (95% CI 0.91; 2.47) and 1.14 (95% CI 0.79; 1.64), respectively. Soot was significantly associated with the risk of URTI at 4-6 months age interval (aOR of 1.45, 95%CI 1.02; 2.09). All air pollutants were also positively associated with lower respiratory tract infection, but all CIs include unity because of relatively small samples. Adjusted odds ratios for gastrointestinal infections were close to unity.

CONCLUSION

Our study adds to the evidence that perinatal exposure to fine particles is associated with respiratory tract infection in infants in a low-middle income country.

Residential exposure to microbial emissions from livestock farms: Implementation and evaluation of land use regression and random forest spatial models

Adverse health effects have been linked with exposure to livestock farms, likely due to airborne microbial agents. Accurate exposure assessment is crucial in epidemiological studies, however limited studies have modelled bioaerosols. This study used measured concentrations in air of livestock commensals (Escherichia coli (E. coli) and Staphylococcus species (spp.)), and antimicrobial resistance genes (tetW and mecA) at 61 residential sites in a livestock-dense region in the Netherlands. For each microbial agent, land use regression (LUR) and random forest (RF) models were developed using Geographic Information System (GIS)-derived livestock-related characteristics as predictors. The mean and standard deviation of annual average concentrations (gene copies/m3) of E. coli, Staphylococcus spp., tetW and mecA were as follows: 38.9 (±1.98), 2574 (±3.29), 20991 (±2.11), and 15.9 (±2.58). Validated through 10-fold cross-validation (CV), the models moderately explained spatial variation of all microbial agents. The best performing model per agent explained respectively 38.4%, 20.9%, 33.3% and 27.4% of the spatial variation of E. coli, Staphylococcus spp., tetW and mecA. RF models had somewhat better performance than LUR models. Livestock predictors related to poultry and pig farms dominated all models. To conclude, the models developed enable enhanced estimates of airborne livestock-related microbial exposure in future epidemiological studies. Consequently, this will provide valuable insights into the public health implications of exposure to specific microbial agents.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Air pollution and human health: a phenome-wide association study

OBJECTIVES

To explore the associations of long-term exposure to air pollution with onset of all human health conditions.

DESIGN

Prospective phenome-wide association study.

SETTING

Denmark.

PARTICIPANTS

All Danish residents aged ≥30 years on 1 January 2000 were included (N=3 323 612). After exclusion of individuals with missing geocoded residential addresses, 3 111 988 participants were available for the statistical analyses.

MAIN OUTCOME MEASURE

First registered diagnosis of every health condition according to the International Classification of Diseases, 10th revision, from 2000 to 2017.

RESULTS

Long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were both positively associated with the onset of more than 700 health conditions (ie, >80% of the registered health conditions) after correction for multiple testing, while the remaining associations were inverse or insignificant. As regards the most common health conditions, PM2.5 and NO2 were strongest positively associated with chronic obstructive pulmonary disease (PM2.5: HR 1.06 (95% CI 1.05 to 1.07) per 1 IQR increase in exposure level; NO2: 1.14 (95% CI 1.12 to 1.15)), type 2 diabetes (PM2.5: 1.06 (95% CI 1.05 to 1.06); NO2: 1.12 (95% CI 1.10 to 1.13)) and ischaemic heart disease (PM2.5: 1.05 (95% CI 1.04 to 1.05); NO2: 1.11 (95% CI 1.09 to 1.12)). Furthermore, PM2.5 and NO2 were both positively associated with so far unexplored, but highly prevalent outcomes relevant to public health, including senile cataract, hearing loss and urinary tract infection.

CONCLUSIONS

The findings of this study suggest that air pollution has a more extensive impact on human health than previously known. However, as this study is the first of its kind to investigate the associations of long-term exposure to air pollution with onset of all human health conditions, further research is needed to replicate the study findings.

Long-term exposure to several constituents and sources of PM2.5 is associated with incidence of upper aerodigestive tract cancers but not gastric cancer: Results from the large pooled European cohort of the ELAPSE project

It is unclear whether cancers of the upper aerodigestive tract (UADT) and gastric cancer are related to air pollution, due to few studies with inconsistent results. The effects of particulate matter (PM) may vary across locations due to different source contributions and related PM compositions, and it is not clear which PM constituents/sources are most relevant from a consideration of overall mass concentration alone. We therefore investigated the association of UADT and gastric cancers with PM2.5 elemental constituents and sources components indicative of different sources within a large multicentre population based epidemiological study. Cohorts with at least 10 cases per cohort led to ten and eight cohorts from five countries contributing to UADT- and gastric cancer analysis, respectively. Outcome ascertainment was based on cancer registry data or data of comparable quality. We assigned home address exposure to eight elemental constituents (Cu, Fe, K, Ni, S, Si, V and Zn) estimated from Europe-wide exposure models, and five source components identified by absolute principal component analysis (APCA). Cox regression models were run with age as time scale, stratified for sex and cohort and adjusted for relevant individual and neighbourhood level confounders. We observed 1139 UADT and 872 gastric cancer cases during a mean follow-up of 18.3 and 18.5 years, respectively. UADT cancer incidence was associated with all constituents except K in single element analyses. After adjustment for NO2, only Ni and V remained associated with UADT. Residual oil combustion and traffic source components were associated with UADT cancer persisting in the multiple source model. No associations were found for any of the elements or source components and gastric cancer incidence. Our results indicate an association of several PM constituents indicative of different sources with UADT but not gastric cancer incidence with the most robust evidence for traffic and residual oil combustion.

Long-term exposure to ambient air pollution and risk of leukemia and lymphoma in a pooled European cohort

Leukemia and lymphoma are the two most common forms of hematologic malignancy, and their etiology is largely unknown. Pathophysiological mechanisms suggest a possible association with air pollution, but little empirical evidence is available. We aimed to investigate the association between long-term residential exposure to outdoor air pollution and risk of leukemia and lymphoma. We pooled data from four cohorts from three European countries as part of the "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration. We used Europe-wide land use regression models to assess annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone (O3) at residences. We also estimated concentrations of PM2.5 elemental components: copper (Cu), iron (Fe), zinc (Zn); sulfur (S); nickel (Ni), vanadium (V), silicon (Si) and potassium (K). We applied Cox proportional hazards models to investigate the associations. Among the study population of 247,436 individuals, 760 leukemia and 1122 lymphoma cases were diagnosed during 4,656,140 person-years of follow-up. The results showed a leukemia hazard ratio (HR) of 1.13 (95% confidence intervals [CI]: 1.01-1.26) per 10 μg/m3 NO2, which was robust in two-pollutant models and consistent across the four cohorts and according to smoking status. Sex-specific analyses suggested that this association was confined to the male population. Further, the results showed increased lymphoma HRs for PM2.5 (HR = 1.16; 95% CI: 1.02-1.34) and potassium content of PM2.5, which were consistent in two-pollutant models and according to sex. Our results suggest that air pollution at the residence may be associated with adult leukemia and lymphoma.

Short-term residential exposure to endotoxin emitted from livestock farms in relation to lung function in non-farming residents

BACKGROUND

Evidence on the public health relevance of exposure to livestock farm emissions is increasing. Research mostly focused on chemical air pollution, less on microbial exposure, while endotoxins are suggested relevant bacterial components in farm emissions. Acute respiratory health effects of short-term exposure to livestock-related air pollution has been shown for NH3 and PM10, but has not yet been studied for endotoxin. We aimed to assess associations between lung function and short-term exposure to livestock farming emitted endotoxin in co-pollutant models with NH3 and PM10.

METHODS

In 2014/2015, spirometry was conducted in 2308 non-farming residents living in a rural area in the Netherlands. Residential exposure to livestock farming emitted endotoxin during the week prior to spirometry was estimated by dispersion modelling. The model was applied to geo-located individual barns within 10 km of each home address using provincial farm data and local hourly meteorological conditions. Regional week-average measured concentrations of NH3 and PM10 were obtained through monitoring stations. Lung function parameters (FEV1, FVC, FEV1/FVC, MMEF) were expressed in %-predicted value based on GLI-2012. Exposure-response analyses were performed by linear regression modelling.

RESULTS

Week-average endotoxin exposure was negatively associated with FVC, independently from regional NH3 and PM10 exposure. A 1.1% decline in FVC was estimated for an increase of endotoxin exposure from 10th to 90th percentile. Stratified analyses showed a larger decline (3.2%) for participants with current asthma and/or COPD. FEV1 was negatively associated with week-average endotoxin exposure, but less consistent after co-pollutant adjustment. FEV1/FVC and MMEF were not associated with week-average endotoxin exposure.

CONCLUSIONS

Lower lung function in non-farming residents was observed in relation to short-term residential exposure to livestock farming emitted endotoxin. This study indicates the probable relevance of exposure to microbial emissions from livestock farms considering public health besides chemical air pollution, necessitating future research incorporating both.

Outdoor Ultrafine Particulate Matter and Risk of Lung Cancer in Southern California

Rationale: Particulate matter ⩽2.5 μm in aerodynamic diameter (PM2.5) is an established cause of lung cancer, but the association with ultrafine particulate matter (UFP; aerodynamic diameter < 0.1 μm) is unclear. Objectives: To investigate the association between UFP and lung cancer overall and by histologic subtype. Methods: The Los Angeles Ultrafines Study includes 45,012 participants aged ⩾50 years in southern California at enrollment (1995-1996) followed through 2017 for incident lung cancer (n = 1,770). We estimated historical residential ambient UFP number concentrations via land use regression and back extrapolation using PM2.5. In Cox proportional hazards models adjusted for smoking and other confounders, we estimated associations between 10-year lagged UFP (per 10,000 particles/cm3 and quartiles) and lung cancer overall and by major histologic subtype (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma). We also evaluated relationships by smoking status, birth cohort, and historical duration at the residence. Measurements and Main Results: UFP was modestly associated with lung cancer risk overall (hazard ratio [HR], 1.03 [95% confidence interval (CI), 0.99-1.08]). For adenocarcinoma, we observed a positive trend among men; risk was increased in the highest exposure quartile versus the lowest (HR, 1.39 [95% CI, 1.05-1.85]; P for trend = 0.01) and was also increased in continuous models (HR per 10,000 particles/cm3, 1.09 [95% CI, 1.00-1.18]), but no increased risk was apparent among women (P for interaction = 0.03). Adenocarcinoma risk was elevated among men born between 1925 and 1930 (HR, 1.13 [95% CI, 1.02-1.26] per 10,000) but not for other birth cohorts, and was suggestive for men with ⩾10 years of residential duration (HR, 1.11 [95% CI, 0.98-1.26]). We found no consistent associations for women or other histologic subtypes. Conclusions: UFP exposure was modestly associated with lung cancer overall, with stronger associations observed for adenocarcinoma of the lung.

Mobile monitoring of air pollutants; performance evaluation of a mixed-model land use regression framework in relation to the number of drive days

We used black carbon data from a mobile monitoring campaign in Oakland, USA measuring street segments up to 40 times and compared a data-only, LUR model and mixed-model approach with a long-term average, represented by the average concentration based on 40 drive days on that street segment. The mixed model outperformed the data-only and LUR model estimates, with 80% explained variance after 5 drive days and 90% after 14 drive days. The data-only approach needed 8 and 15 to achieve an explained variance of 80% and 90%, respectively, The LUR model never achieved an explained variance higher than 70%. The mixed model is a scalable approach, as it can be used before all street segments in a domain are measured by developing a LUR model and adds information with increasing repeats per street segment.

Implementing co-created citizen science in five environmental epidemiological studies in the CitieS-Health project

BACKGROUND AND AIM

Scientists and scientific institutions are adopting more extensive participatory models, hoping to revisit the existing relationship between science and society. Though citizen science has become more common in environmental monitoring, it is seldom utilized in environmental epidemiology. In the CitieS-Health project, we co-created epidemiological studies with citizens in five European countries. The aim of this paper is to share our experiences and impart methodological insight into the application of co-created citizen science strategies in environmental epidemiology.

METHODS

We applied the CitieS-Health framework, involving citizens in all the phases of the studies: identifying research questions, designing research protocols, collecting data, analysing data, interpreting data, formulating conclusions, authoring scientific articles and communicating the results to diverse audiences. These epidemiological studies, conducted in specific areas in Italy, Lithuania, the Netherlands, Slovenia and Spain, covered diverse local environmental issues and health effects ranging from air pollution and mental health to industrial pollution and kidney disease.

RESULTS

Together with citizens, we successfully conducted environmental epidemiological studies that generated new scientific knowledge reflecting the concerns and knowledge of citizens. Citizens contributed in all the research activities, including activities beyond formulating the research questions, though the researchers initiated several design discussions and conducted time-consuming and complex tasks (e.g. data analysis, measurement of specific exposures and health outcomes). The challenges we encountered were engaging effectively with citizens throughout the study, harmonizing citizens' knowledge and values with the academics' expertise, managing civic expectations, making complex concepts understandable to citizens and representativeness of participating citizens. The co-created studies were able to empower citizens to address local health concerns by sharing and using scientific knowledge generated from studies.

CONCLUSIONS

Integration of co-created citizen science in environmental epidemiology is feasible and has the potential to improve the quality of research whilst promoting civic trust in research and results.

Ethnic and socioeconomic inequalities in air pollution exposure: a cross-sectional analysis of nationwide individual-level data from the Netherlands

BACKGROUND

Air pollution contributes to a large disease burden and some populations are disproportionately exposed. We aimed to evaluate ethnic and socioeconomic differences in exposure to air pollution in the Netherlands.

METHODS

We did a nationwide, cross-sectional analysis of all residents of the Netherlands on Jan 1, 2019. Sociodemographic information was centralised by Statistics Netherlands and mainly originated from the National Population Register, the tax register, and education registers. Concentrations of NO2, PM2·5, PM10, and elemental carbon, modelled by the National Institute for Public Health and the Environment, were linked to the individual-level demographic data. We assessed differences in air pollution exposures across the 40 largest minority ethnic groups. Evaluation of how ethnicity intersected with socioeconomic position in relation to exposures was done for the ten largest ethnic groups, plus Chinese and Indian groups, in both urban and rural areas using multivariable linear regression analyses.

FINDINGS

The total study population consisted of 17 251 511 individuals. Minority ethnic groups were consistently exposed to higher levels of air pollution than the ethnic Dutch population. The magnitude of inequalities varied between the minority ethnic groups, with 3-44% higher exposures to NO2 and 1-9% higher exposures to PM2·5 compared with the ethnic Dutch group. Average exposures were highest for the lowest socioeconomic group. Ethnic inequalities in exposure remained after adjustment for socioeconomic position and were of similar magnitude in urban and rural areas.

INTERPRETATION

The variability in air pollution exposure across ethnic and socioeconomic subgroups in the Netherlands indicates environmental injustice at the intersection of social characteristics. The health consequences of the observed inequalities and the underlying processes driving them warrant further investigation.

FUNDING

The Gravitation programme of the Dutch Ministry of Education, Culture, and Science, the Netherlands Organization for Scientific Research, the Netherlands Organisation for Health Research and Development, and Amsterdam University Medical Center.

Multiple myeloma risk in relation to long-term air pollution exposure - A pooled analysis of four European cohorts

BACKGROUND

Air pollution is a growing concern worldwide, with significant impacts on human health. Multiple myeloma is a type of blood cancer with increasing incidence. Studies have linked air pollution exposure to various types of cancer, including leukemia and lymphoma, however, the relationship with multiple myeloma incidence has not been extensively investigated.

METHODS

We pooled four European cohorts (N = 234,803) and assessed the association between residential exposure to nitrogen dioxide (NO2), fine particles (PM2.5), black carbon (BC), and ozone (O3) and multiple myeloma. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level.

RESULTS

During 4,415,817 person-years of follow-up (average 18.8 years), we observed 404 cases of multiple myeloma. The results of the fully adjusted linear analyses showed hazard ratios (95% confidence interval) of 0.99 (0.84, 1.16) per 10 μg/m³ NO2, 1.04 (0.82, 1.33) per 5 μg/m³ PM2.5, 0.99 (0.84, 1.18) per 0.5 10-5 m-1 BCE, and 1.11 (0.87, 1.41) per 10 μg/m³ O3.

CONCLUSIONS

We did not observe an association between long-term ambient air pollution exposure and incidence of multiple myeloma.

Long-Term Exposure to Low-Level PM2.5 and Mortality: Investigation of Heterogeneity by Harmonizing Analyses in Large Cohort Studies in Canada, United States, and Europe

BACKGROUND

Studies across the globe generally reported increased mortality risks associated with particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) exposure with large heterogeneity in the magnitude of reported associations and the shape of concentration-response functions (CRFs). We aimed to evaluate the impact of key study design factors (including confounders, applied exposure model, population age, and outcome definition) on PM 2.5 effect estimates by harmonizing analyses on three previously published large studies in Canada [Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE), 1991-2016], the United States (Medicare, 2000-2016), and Europe [Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), 2000-2016] as much as possible.

METHODS

We harmonized the study populations to individuals 65 + years of age, applied the same satellite-derived PM 2.5 exposure estimates, and selected the same sets of potential confounders and the same outcome. We evaluated whether differences in previously published effect estimates across cohorts were reduced after harmonization among these factors. Additional analyses were conducted to assess the influence of key design features on estimated risks, including adjusted covariates and exposure assessment method. A combined CRF was assessed with meta-analysis based on the extended shape-constrained health impact function (eSCHIF).

RESULTS

More than 81 million participants were included, contributing 692 million person-years of follow-up. Hazard ratios and 95% confidence intervals (CIs) for all-cause mortality associated with a 5 - μ g / m 3 increase in PM 2.5 were 1.039 (1.032, 1.046) in MAPLE, 1.025 (1.021, 1.029) in Medicare, and 1.041 (1.014, 1.069) in ELAPSE. Applying a harmonized analytical approach marginally reduced difference in the observed associations across the three studies. Magnitude of the association was affected by the adjusted covariates, exposure assessment methodology, age of the population, and marginally by outcome definition. Shape of the CRFs differed across cohorts but generally showed associations down to the lowest observed PM 2.5 levels. A common CRF suggested a monotonically increased risk down to the lowest exposure level. https://doi.org/10.1289/EHP12141.

Evidence Synthesis of Observational Studies in Environmental Health: Lessons Learned from a Systematic Review on Traffic-Related Air Pollution

BACKGROUND

There is a long tradition in environmental health of using frameworks for evidence synthesis, such as those of the U.S. Environmental Protection Agency for its Integrated Science Assessments and the International Agency for Research on Cancer Monographs. The framework, Grading of Recommendations Assessment, Development, and Evaluation (GRADE), was developed for evidence synthesis in clinical medicine. The U.S. Office of Health Assessment and Translation (OHAT) elaborated an approach for evidence synthesis in environmental health building on GRADE.

METHODS

We applied a modified OHAT approach and a broader "narrative" assessment to assess the level of confidence in a large systematic review on traffic-related air pollution and health outcomes.

DISCUSSION

We discuss several challenges with the OHAT approach and its implementation and suggest improvements for synthesizing evidence from observational studies in environmental health. We consider the determination of confidence using a formal rating scheme of up- and downgrading of certain factors, the treatment of every factor as equally important, and the lower initial confidence rating of observational studies to be fundamental issues in the OHAT approach. We argue that some observational studies can offer high-confidence evidence in environmental health. We note that heterogeneity in magnitude of effect estimates should generally not weaken the confidence in the evidence, and consistency of associations across study designs, populations, and exposure assessment methods may strengthen confidence in the evidence. We mention that publication bias should be explored beyond statistical methods and is likely limited when large and collaborative studies comprise most of the evidence and when accrued over several decades. We propose to identify possible key biases, their most likely direction, and their potential impacts on the results. We think that the OHAT approach and other GRADE-type frameworks require substantial modification to align better with features of environmental health questions and the studies that address them. We emphasize that a broader, "narrative" evidence assessment based on the systematic review may complement a formal GRADE-type evaluation. https://doi.org/10.1289/EHP11532.

Long-Term Exposure to AIR Pollution and COVID-19 Mortality and Morbidity in DENmark: Who Is Most Susceptible? (AIRCODEN)

INTRODUCTION

Early ecological studies have suggested a link between air pollution and Coronavirus Diseases 2019 (COVID-19); however, the evidence from individual-level prospective cohort studies is still sparse. Here, we have examined, in a general population, whether long-term exposure to air pollution is associated with the risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and developing severe COVID-19, resulting in hospitalization or death and who is most susceptible. We also examined whether long-term exposure to air pollution is associated with hospitalization or death due to COVID-19 in those who have tested positive for SARS-CoV-2.

METHODS

We included all Danish residents 30 years or older who resided in Denmark on March 1, 2020. and followed them in the National COVID-19 Surveillance System until first positive test (incidence), COVID-19 hospitalization, or death until April 26, 2021. We estimated mean levels of nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter <2.5 μm (PM2.5), black carbon (BC), and ozone (O3) at cohort participants' residence in 2019 by the Danish Eulerian Hemispheric Model/Urban Background Model. We used Cox proportional hazard models to estimate the associations of air pollutants with COVID-19 incidence, hospitalization, and mortality adjusting for age, sex, and socioeconomic status (SES) at the individual and area levels. We examined effect modification by age, sex, SES (education, income, wealth, employment), and comorbidities with cardiovascular disease, respiratory disease, acute lower respiratory infections, diabetes, lung cancer, and dementia. We used logistic regression to examine association of air pollutants with COVID-19-related hospitalization or death among SARS-CoV-2 positive patients, adjusting for age, sex, individual- and area-level SES.

RESULTS

Of 3,721,810 people, 138,742 were infected, 11,270 hospitalized, and 2,557 died from COVID-19 during 14 months of follow-up. We detected strong positive associations with COVID-19 incidence, with hazard ratio (HR) and 95% confidence interval (CI) of 1.10 (CI: 1.05-1.14) per 0.5-μg/m3 increase in PM2.5 and 1.18 (CI: 1.14-1.23) per 3.6-μg/m3 increase in NO2. For COVID-19 hospitalizations and for COVID-19 deaths, corresponding HRs and 95% CIs were 1.09 (CI: 1.01-1.17) and 1.19 (CI: 1.12-1.27), respectively for PM2.5, and 1.23 (CI: 1.04-1.44) and 1.18 (CI: 1.03-1.34), respectively for NO2. We also found strong positive and statistically significant associations with BC and negative associations with O3. Associations were strongest in those aged 65 years old or older, participants with the lowest SES, and patients with chronic cardiovascular, respiratory, metabolic, lung cancer, and neurodegenerative disease. Among 138,742 individuals who have tested positive for SARS-Cov-2, we detected positive association with COVID-19 hospitalizations (N = 11,270) with odds ratio and 95% CI of 1.04 (CI: 1.01- 1.08) per 0.5-μg/m3 increase in PM2.5 and 1.06 (CI: 1.01-1.12) per 3.6-μg/m3 increase in NO2, but no association with PM with an aerodynamic diameter <10 μm (PM10), BC, or O3, and no association between any of the pollutants and COVID-19 mortality (N = 2,557).

CONCLUSIONS

This large nationwide study provides strong new evidence in support of association between long-term exposure to air pollution and COVID-19.

Clean air in Europe for all! Taking stock of the proposed revision to the ambient air quality directives: a joint ERS, HEI and ISEE workshop report

Ambient air pollution is a major public health concern and comprehensive new legislation is currently being considered to improve air quality in Europe. The European Respiratory Society (ERS), Health Effects Institute (HEI), and International Society for Environmental Epidemiology (ISEE) organised a joint meeting on May 24, 2023 in Brussels, Belgium, to review and critically evaluate the latest evidence on the health effects of air pollution and discuss ongoing revisions of the European Ambient Air Quality Directives (AAQDs). A multi-disciplinary expert group of air pollution and health researchers, patient and medical societies, and policy representatives participated. This report summarises key discussions at the meeting.

Long-term air pollution exposure and malignant intracranial tumours of the central nervous system: a pooled analysis of six European cohorts

BACKGROUND

Risk factors for malignant tumours of the central nervous system (CNS) are largely unknown.

METHODS

We pooled six European cohorts (N = 302,493) and assessed the association between residential exposure to nitrogen dioxide (NO2), fine particles (PM2.5), black carbon (BC), ozone (O3) and eight elemental components of PM2.5 (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) and malignant intracranial CNS tumours defined according to the International Classification of Diseases ICD-9/ICD-10 codes 192.1/C70.0, 191.0-191.9/C71.0-C71.9, 192.0/C72.2-C72.5. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level.

RESULTS

During 5,497,514 person-years of follow-up (average 18.2 years), we observed 623 malignant CNS tumours. The results of the fully adjusted linear analyses showed a hazard ratio (95% confidence interval) of 1.07 (0.95, 1.21) per 10 μg/m³ NO2, 1.17 (0.96, 1.41) per 5 μg/m³ PM2.5, 1.10 (0.97, 1.25) per 0.5 10-5m-1 BC, and 0.99 (0.84, 1.17) per 10 μg/m³ O3.

CONCLUSIONS

We observed indications of an association between exposure to NO2, PM2.5, and BC and tumours of the CNS. The PM elements were not consistently associated with CNS tumour incidence.

Integrating large-scale stationary and local mobile measurements to estimate hyperlocal long-term air pollution using transfer learning methods

Mobile air quality measurements are collected typically for several seconds per road segment and in specific timeslots (e.g., working hours). These short-term and on-road characteristics of mobile measurements become the ubiquitous shortcomings of applying land use regression (LUR) models to estimate long-term concentrations at residential addresses. This issue was previously found to be mitigated by transferring LUR models to the long-term residential domain using routine long-term measurements in the studied region as the transfer target (local scale). However, long-term measurements are generally sparse in individual cities. For this scenario, we propose an alternative by taking long-term measurements collected over a larger geographical area (global scale) as the transfer target and local mobile measurements as the source (Global2Local model). We empirically tested national, airshed countries (i.e., national plus neighboring countries) and Europe as the global scale in developing Global2Local models to map nitrogen dioxide (NO₂) concentrations in Amsterdam. The airshed countries scale provided the lowest absolute errors, and the Europe-wide scale had the highest R². Compared to a "global" LUR model (trained exclusively with European-wide long-term measurements), and a local mobile LUR model (using mobile data from Amsterdam only), the Global2Local model significantly reduced the absolute error of the local mobile LUR model (root-mean-square error, 6.9 vs 12.6 μg/m³) and improved the percentage explained variances compared to the global model (R², 0.43 vs 0.28, assessed by independent long-term NO₂ measurements in Amsterdam, n = 90). The Global2Local method improves the generalizability of mobile measurements in mapping long-term residential concentrations with a fine spatial resolution, which is preferred in environmental epidemiological studies.

Spatial and sector-specific contributions of emissions to ambient air pollution and mortality in European cities: a health impact assessment

BACKGROUND

Ambient air pollution is a major risk to health and wellbeing in European cities. We aimed to estimate spatial and sector-specific contributions of emissions to ambient air pollution and evaluate the effects of source-specific reductions in pollutants on mortality in European cities to support targeted source-specific actions to address air pollution and promote population health.

METHODS

We conducted a health impact assessment of data from 2015 for 857 European cities to estimate source contributions to annual PM_2·5 and NO₂ concentrations using the Screening for High Emission Reduction Potentials for Air quality tool. We evaluated contributions from transport, industry, energy, residential, agriculture, shipping, and aviation, other, natural, and external sources. For each city and sector, three spatial levels were considered: contributions from the same city, the rest of the country, and transboundary. Mortality effects were estimated for adult populations (ie, ≥20 years) following standard comparative risk assessment methods to calculate the annual mortality preventable on spatial and sector-specific reductions in PM_2·5 and NO₂.

FINDINGS

We observed strong variability in spatial and sectoral contributions among European cities. For PM_2·5, the main contributors to mortality were the residential (mean contribution of 22·7% [SD 10·2]) and agricultural (18·0% [7·7]) sectors, followed by industry (13·8% [6·0]), transport (13·5% [5·8]), energy (10·0% [6·4]), and shipping (5·5% [5·7]). For NO₂, the main contributor to mortality was transport (48·5% [SD 15·2]), with additional contributions from industry (15·0% [10·8]), energy (14·7% [12·9]), residential (10·3% [5·0]), and shipping (9·7% [12·7]). The mean city contribution to its own air pollution mortality was 13·5% (SD 9·9) for PM_2·5 and 34·4% (19·6) for NO₂, and contribution increased among cities of largest area (22·3% [12·2] for PM_2·5 and 52·2% [19·4] for NO₂) and among European capitals (29·9% [12·5] for PM_2·5 and 62·7% [14·7] for NO₂).

INTERPRETATION

We estimated source-specific air pollution health effects at the city level. Our results show strong variability, emphasising the need for local policies and coordinated actions that consider city-level specificities in source contributions.

FUNDING

Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making 2023-2026 Horizon Europe project.

Long-term exposure to air pollution and risk of SARS-CoV-2 infection and COVID-19 hospitalisation or death: Danish nationwide cohort study

BACKGROUND

Early ecological studies have suggested links between air pollution and risk of coronavirus disease 2019 (COVID-19), but evidence from individual-level cohort studies is still sparse. We examined whether long-term exposure to air pollution is associated with risk of COVID-19 and who is most susceptible.

METHODS

We followed 3 721 810 Danish residents aged ≥30 years on 1 March 2020 in the National COVID-19 Surveillance System until the date of first positive test (incidence), COVID-19 hospitalisation or death until 26 April 2021. We estimated residential annual mean particulate matter with diameter ≤2.5 μm (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone (O3) in 2019 by the Danish DEHM/UBM model, and used Cox proportional hazards regression models to estimate the associations of air pollutants with COVID-19 outcomes, adjusting for age, sex, individual- and area-level socioeconomic status, and population density.

RESULTS

138 742 individuals were infected, 11 270 were hospitalised and 2557 died from COVID-19 during 14 months. We detected associations of PM2.5 (per 0.53 μg·m-3) and NO2 (per 3.59 μg·m-3) with COVID-19 incidence (hazard ratio (HR) 1.10 (95% CI 1.05-1.14) and HR 1.18 (95% CI 1.14-1.23), respectively), hospitalisations (HR 1.09 (95% CI 1.01-1.17) and HR 1.19 (95% CI 1.12-1.27), respectively) and death (HR 1.23 (95% CI 1.04-1.44) and HR 1.18 (95% CI 1.03-1.34), respectively), which were strongest in the lowest socioeconomic groups and among patients with chronic respiratory, cardiometabolic and neurodegenerative diseases. We found positive associations with BC and negative associations with O3.

CONCLUSION

Long-term exposure to air pollution may contribute to increased risk of contracting severe acute respiratory syndrome coronavirus 2 infection as well as developing severe COVID-19 disease requiring hospitalisation or resulting in death.

Comparison of traditional Cox regression and causal modeling to investigate the association between long-term air pollution exposure and natural-cause mortality within European cohorts

Most studies investigating the health effects of long-term exposure to air pollution used traditional regression models, although causal inference approaches have been proposed as alternative. However, few studies have applied causal models and comparisons with traditional methods are sparse. We therefore compared the associations between natural-cause mortality and exposure to fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) using traditional Cox and causal models in a large multicenter cohort setting. We analysed data from eight well-characterized cohorts (pooled cohort) and seven administrative cohorts from eleven European countries. Annual mean PM_2.5 and NO₂ from Europe-wide models were assigned to baseline residential addresses and dichotomized at selected cut-off values (PM_2.5: 10, 12, 15 μg/m³; NO₂: 20, 40 μg/m³). For each pollutant, we estimated the propensity score as the conditional likelihood of exposure given available covariates, and derived corresponding inverse-probability weights (IPW). We applied Cox proportional hazards models i) adjusting for all covariates ("traditional Cox") and ii) weighting by IPW ("causal model"). Of 325,367 and 28,063,809 participants in the pooled and administrative cohorts, 47,131 and 3,580,264 died from natural causes, respectively. For PM_2.5 above vs. below 12 μg/m³, the hazard ratios (HRs) of natural-cause mortality were 1.17 (95% CI 1.13-1.21) and 1.15 (1.11-1.19) for the traditional and causal models in the pooled cohort, and 1.03 (1.01-1.06) and 1.02 (0.97-1.09) in the administrative cohorts. For NO₂ above vs below 20 μg/m³, the HRs were 1.12 (1.09-1.14) and 1.07 (1.05-1.09) for the pooled and 1.06 (95% CI 1.03-1.08) and 1.05 (1.02-1.07) for the administrative cohorts. In conclusion, we observed mostly consistent associations between long-term air pollution exposure and natural-cause mortality with both approaches, though estimates partly differed in individual cohorts with no systematic pattern. The application of multiple modelling methods might help to improve causal inference. 299 of 300 words.

Long-Term Exposure to Traffic-Related Air Pollution and Diabetes: A Systematic Review and Meta-Analysis

Objectives: We report results of a systematic review on the health effects of long-term traffic-related air pollution (TRAP) and diabetes in the adult population. Methods: An expert Panel appointed by the Health Effects Institute conducted this systematic review. We searched the PubMed and LUDOK databases for epidemiological studies from 1980 to July 2019. TRAP was defined based on a comprehensive protocol. Random-effects meta-analyses were performed. Confidence assessments were based on a modified Office for Health Assessment and Translation (OHAT) approach, complemented with a broader narrative synthesis. We extended our interpretation to include evidence published up to May 2022. Results: We considered 21 studies on diabetes. All meta-analytic estimates indicated higher diabetes risks with higher exposure. Exposure to NO₂ was associated with higher diabetes prevalence (RR 1.09; 95% CI: 1.02; 1.17 per 10 μg/m³), but less pronounced for diabetes incidence (RR 1.04; 95% CI: 0.96; 1.13 per 10 μg/m³). The overall confidence in the evidence was rated moderate, strengthened by the addition of 5 recently published studies. Conclusion: There was moderate evidence for an association of long-term TRAP exposure with diabetes.

Long-term exposure to ultrafine particles and natural and cause-specific mortality

BACKGROUND

Health implications of long-term exposure to ubiquitously present ultrafine particles (UFP) are uncertain. The aim of this study was to investigate the associations between long-term UFP exposure and natural and cause-specific mortality (including cardiovascular disease (CVD), respiratory disease, and lung cancer) in the Netherlands.

METHODS

A Dutch national cohort of 10.8 million adults aged ≥ 30 years was followed from 2013 until 2019. Annual average UFP concentrations were estimated at the home address at baseline, using land-use regression models based on a nationwide mobile monitoring campaign performed at the midpoint of the follow-up period. Cox proportional hazard models were applied, adjusting for individual and area-level socio-economic status covariates. Two-pollutant models with the major regulated pollutants nitrogen dioxide (NO₂) and fine particles (PM_2.5 and PM₁₀), and the health relevant combustion aerosol pollutant (elemental carbon (EC)) were assessed based on dispersion modelling.

RESULTS

A total of 945,615 natural deaths occurred during 71,008,209 person-years of follow-up. The correlation of UFP concentration with other pollutants ranged from moderate (0.59 (PM_2.5)) to high (0.81 (NO₂)). We found a significant association between annual average UFP exposure and natural mortality [HR 1.012 (95 % CI 1.010-1.015), per interquartile range (IQR) (2723 particles/cm³) increment]. Associations were stronger for respiratory disease mortality [HR 1.022 (1.013-1.032)] and lung cancer mortality [HR 1.038 (1.028-1.048)] and weaker for CVD mortality [HR 1.005 (1.000-1.011)]. The associations of UFP with natural and lung cancer mortality attenuated but remained significant in all two-pollutant models, whereas the associations with CVD and respiratory mortality attenuated to the null.

CONCLUSION

Long-term UFP exposure was associated with natural and lung cancer mortality among adults independently from other regulated air pollutants.

Long-term exposure to elemental components of fine particulate matter and all-natural and cause-specific mortality in a Danish nationwide administrative cohort study

BACKGROUND

Fine particulate matter (PM_2.5) is a well-recognized risk factor for premature death. However, evidence on which PM_2.5 components are most relevant is unclear.

METHODS

We evaluated the associations between mortality and long-term exposure to eight PM_2.5 elemental components [copper (Cu), iron (Fe), zinc (Zn), sulfur (S), nickel (Ni), vanadium (V), silicon (Si), and potassium (K)]. Studied outcomes included death from diabetes, chronic kidney disease (CKD), dementia, and psychiatric disorders as well as all-natural causes, cardiovascular disease (CVD), respiratory diseases (RD), and lung cancer. We followed all residents in Denmark (aged ≥30 years) from January 1, 2000 to December 31, 2017. We used European-wide land-use regression models at a 100 × 100 m scale to estimate the residential annual mean levels of exposure to PM_2.5 components. The models were developed with supervised linear regression (SLR) and random forest (RF). The associations were evaluated by Cox proportional hazard models adjusting for individual- and area-level socioeconomic factors and total PM_2.5 mass.

RESULTS

Of 3,081,244 individuals, we observed 803,373 death from natural causes during follow-up. We found significant positive associations between all-natural mortality with Si and K from both exposure modeling approaches (hazard ratios; 95% confidence intervals per interquartile range increase): SLR-Si (1.04; 1.03-1.05), RF-Si (1.01; 1.00-1.02), SLR-K (1.03; 1.02-1.04), and RF-K (1.06; 1.05-1.07). Strong associations of K and Si were detected with most causes of mortality except CKD and K, and diabetes and Si (the strongest associations for psychiatric disorders mortality). In addition, Fe was relevant for mortality from RD, lung cancer, CKD, and psychiatric disorders; Zn with mortality from CKD, RD, and lung cancer, and; Ni and V with lung cancer mortality.

CONCLUSIONS

We present novel results of the relevance of different PM_2.5 components for different causes of death, with K and Si seeming to be most consistently associated with mortality in Denmark.

Exposure to ambient air pollution and elevated blood levels of gamma-glutamyl transferase in a large Austrian cohort

Gamma glutamyl transferase (GGT) is related to oxidative stress and an indicator for liver damage. We investigated the association between air pollution and GGT in a large Austrian cohort (N = 116,109) to better understand how air pollution affects human health. Data come from voluntary prevention visits that were routinely collected within the Vorarlberg Health Monitoring and Prevention Program (VHM&PP). Recruitment was ongoing from 1985 to 2005. Blood was drawn and GGT measured centralized in two laboratories. Land use regression models were applied to estimate individuals' exposure at their home address for particulate matter (PM) with a diameter of <2.5 μm (PM2.5), <10 μm (PM10), fraction between 10 μm and 2.5 μm (PMcoarse), as well as PM2.5 absorbance (PM2.5abs), NO₂, NO_x and eight components of PM. Linear regression models, adjusting for relevant individual and community-level confounders were calculated. The study population was 56 % female with a mean age of 42 years and mean GGT was 19.0 units. Individual PM2.5 and NO₂ exposures were essentially below European limit values of 25 and 40 μg/m³, respectively, with means of 13.58 μg/m³ for PM2.5 and 19.93 μg/m³ for NO₂. Positive associations were observed for PM2.5, PM10, PM2.5abs, NO₂, NO_x, and Cu, K, S in PM2.5 and PM10 fractions and Zn mainly in PM2.5 fraction. The strongest association per interquartile range observed was an increase of serum GGT concentration by 1.40 % (95 %-CI: 0.85 %; 1.95 %) per 45.7 ng/m³ S in PM2.5. Associations were robust to adjustments for other biomarkers, in two-pollutant models and the subset with a stable residential history. We found that long-term exposure to air pollution (PM2.5, PM10, PM2.5abs, NO₂, NO_x) as well as certain elements, were positively associated with baseline GGT levels. The elements associated suggest a role of traffic emissions, long range transport and wood burning.

Long-term exposure to traffic-related air pollution and non-accidental mortality: A systematic review and meta-analysis

BACKGROUND

The health effects of traffic-related air pollution (TRAP) continue to be of important public health interest across the globe. Following its 2010 review, the Health Effects Institute appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected health outcomes. This paper describes the main findings of the systematic review on non-accidental mortality.

METHODS

The Panel used a systematic approach to conduct the review. An extensive search was conducted of literature published between 1980 and 2019. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP, which included studies beyond the near-roadway environment. We performed random-effects meta-analysis when at least three estimates were available of an association between a specific exposure and outcome. We evaluated confidence in the evidence using a modified Office of Health Assessment and Translation (OHAT) approach, supplemented with a broader narrative synthesis.

RESULTS

Thirty-six cohort studies were included. Virtually all studies adjusted for a large number of individual and area-level covariates-including smoking, body mass index, and individual and area-level socioeconomic status-and were judged at a low or moderate risk for bias. Most studies were conducted in North America and Europe, and a few were based in Asia and Australia. The meta-analytic summary estimates for nitrogen dioxide, elemental carbon and fine particulate matter-pollutants with more than 10 studies-were 1.04 (95% CI 1.01, 1.06), 1.02 (1.00, 1.04) and 1.03 (1.01, 1.05) per 10, 1 and 5 µg/m³, respectively. Effect estimates are interpreted as the relative risk of mortality when the exposure differs with the selected increment. The confidence in the evidence for these pollutants was judged as high, because of upgrades for monotonic exposure-response and consistency across populations. The consistent findings across geographical regions, exposure assessment methods and confounder adjustment resulted in a high confidence rating using a narrative approach as well.

CONCLUSIONS

The overall confidence in the evidence for a positive association between long-term exposure to TRAP and non-accidental mortality was high.

Comparing "causal" and "traditional" approaches in the association of long-term exposure to ambient air pollution on mortality: How sensitive are the results?

BACKGROUND

Few comparisons between causal inference and traditional approaches have been performed. We applied "causal" and "traditional" methods to investigate the association between long-term air pollution exposure (PM_2.5 and NO₂) and mortality.

METHODS

We analyzed pooled data from eight well-characterized cohorts and one administrative cohort. We defined the generalized propensity score (GPS) as the conditional likelihood of exposure given confounders, and derived corresponding inverse-probability weights (IPW). We applied Cox-proportional hazard models weighted by IPW, adjusted for GPS, and directly adjusting for all confounders.

RESULTS

In IPW models, PM_2.5 5 µg/m³ increases were associated with hazard ratios (HR) = 1.141 (95% confidence interval (CI): 1.107, 1.176) and 1.050 (1.014, 1.088) in the pooled and administrative cohorts. Corresponding estimates for traditional Cox models were 1.132 (1.107, 1.158) and 1.057 (1.025, 1.089). Almost identical results were found for all approaches and both pollutants, when unbalanced covariates were adjusted for in causal models.

CONCLUSIONS

Traditional and causal approaches provided consistent associations between long-term exposure to air pollution and mortality.

Association between exposure to multiple air pollutants, transportation noise and cause-specific mortality in adults in Switzerland

BACKGROUND

Long-term exposure to air pollution and noise is detrimental to health; but studies that evaluated both remain limited. This study explores associations with natural and cause-specific mortality for a range of air pollutants and transportation noise.

METHODS

Over 4 million adults in Switzerland were followed from 2000 to 2014. Exposure to PM_2.5, PM_2.5 components (Cu, Fe, S and Zn), NO₂, black carbon (BC) and ozone (O₃) from European models, and transportation noise from source-specific Swiss models, were assigned at baseline home addresses. Cox proportional hazards models, adjusted for individual and area-level covariates, were used to evaluate associations with each exposure and death from natural, cardiovascular (CVD) or non-malignant respiratory disease. Analyses included single and two exposure models, and subset analysis to study lower exposure ranges.

RESULTS

During follow-up, 661,534 individuals died of natural causes (36.6% CVD, 6.6% respiratory). All exposures including the PM_2.5 components were associated with natural mortality, with hazard ratios (95% confidence intervals) of 1.026 (1.015, 1.038) per 5 µg/m³ PM_2.5, 1.050 (1.041, 1.059) per 10 µg/m³ NO₂, 1.057 (1.048, 1.067) per 0.5 × 10^-5/m BC and 1.045 (1.040, 1.049) per 10 dB Lden total transportation noise. NO₂, BC, Cu, Fe and noise were consistently associated with CVD and respiratory mortality, whereas PM_2.5 was only associated with CVD mortality. Natural mortality associations persisted < 20 µg/m³ for PM_2.5 and NO₂, < 1.5 10^-5/m BC and < 53 dB Lden total transportation noise. The O₃ association was inverse for all outcomes. Including noise attenuated all outcome associations, though many remained significant. Across outcomes, noise was robust to adjustment to air pollutants (e.g. natural mortality 1.037 (1.033, 1.042) per 10 dB Lden total transportation noise, after including BC).

CONCLUSION

Long-term exposure to air pollution and transportation noise in Switzerland contribute to premature mortality. Considering co-exposures revealed the importance of local traffic-related pollutants such as NO₂, BC and transportation noise.

The effect of exposure to traffic related air pollutants in pregnancy on birth anthropometry: a cohort study in a heavily polluted low-middle income country

BACKGROUND

Ambient air pollution has been recognized as one of the most important environmental health threats. Exposure in early life may affect pregnancy outcomes and the health of the offspring. The main objective of our study was to assess the association between prenatal exposure to traffic related air pollutants during pregnancy on birth weight and length. Second, to evaluate the association between prenatal exposure to traffic related air pollutants and the risk of low birth weight (LBW).

METHODS

Three hundred forty mother-infant pairs were included in this prospective cohort study performed in Jakarta, March 2016-September 2020. Exposure to outdoor PM_2.5, soot, NO_x, and NO₂ was assessed by land use regression (LUR) models at individual level. Multiple linear regression models were built to evaluate the association between air pollutants with birth weight (BW) and birth length (BL). Logistic regression was used to assess the risk of low birth weight (LBW) associated with all air pollutants.

RESULTS

The average PM_2.5 concentration was almost eight times higher than the current WHO guideline and the NO₂ level was three times higher. Soot and NO_x were significantly associated with reduced birth length. Birth length was reduced by - 3.83 mm (95% CI -6.91; - 0.75) for every IQR (0.74 × 10^- 5 per m) increase of soot, and reduced by - 2.82 mm (95% CI -5.33;-0.30) for every IQR (4.68 μg/m³) increase of NO_x. Outdoor air pollutants were not significantly associated with reduced birth weight nor the risk of LBW.

CONCLUSION

Exposure to soot and NO_x during pregnancy was associated with reduced birth length. Associations between exposure to all air pollutants with birth weight and the risk of LBW were less convincing.

Exposure to ambient ultrafine particles and allergic sensitization in children up to 16 years

BACKGROUND

Few epidemiological studies so far have investigated the role of long-term exposure to ultrafine particles (UFP) in inhalant and food allergy development.

OBJECTIVES

The purpose of this study was to assess the association between UFP exposure and allergic sensitization to inhalant and food allergens in children up to 16 years old in the Netherlands.

METHODS

2295 participants of a prospective birth cohort with IgE measurements to common inhalant and food allergens at ages 4, 8, 12 and/or 16 were included in the study. Annual average UFP concentrations were estimated for the home addresses at birth and at the time of the IgE measurements using land-use regression models. Generalized estimating equations were used for the assessment of overall and age-specific associations between UFP exposure and allergic sensitization. Additionally, single- and two-pollutant models with NO₂, PM_2.5, PM_2.5 absorbance and PM₁₀ were assessed.

RESULTS

We found no significant associations between UFP exposure and allergic sensitization to inhalant and food allergens (OR (95% CI) ranging from 1.02 (0.95-1.10) to 1.05 (0.98-1.12), per IQR increment). NO₂, PM_2.5, PM_2.5 absorbance and PM₁₀ showed significant associations with sensitization to food allergens (OR (95% CI) ranging from 1.09 (1.00-1.20) to 1.23 (1.06-1.43) per IQR increment). NO₂, PM_2.5, PM_2.5 absorbance and PM₁₀ were not associated with sensitization to inhalant allergens. For NO₂, PM_2.5 and PM_2.5 absorbance, the associations with sensitization to food allergens persisted in two-pollutant models with UFP.

CONCLUSION

This study found no association between annual average exposure to UFP and allergic sensitization in children up to 16 years of age. NO₂, PM_2.5, PM_2.5 absorbance and PM₁₀ were associated with sensitization to food allergens.

Breast Cancer Incidence in Relation to Long-Term Low-Level Exposure to Air Pollution in the ELAPSE Pooled Cohort

BACKGROUND

Established risk factors for breast cancer include genetic disposition, reproductive factors, hormone therapy, and lifestyle-related factors such as alcohol consumption, physical inactivity, smoking, and obesity. More recently a role of environmental exposures, including air pollution, has also been suggested. The aim of this study, was to investigate the relationship between long-term air pollution exposure and breast cancer incidence.

METHODS

We conducted a pooled analysis among six European cohorts (n = 199,719) on the association between long-term residential levels of ambient nitrogen dioxide (NO2), fine particles (PM2.5), black carbon (BC), and ozone in the warm season (O3) and breast cancer incidence in women. The selected cohorts represented the lower range of air pollutant concentrations in Europe. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level.

RESULTS

During 3,592,885 person-years of follow-up, we observed a total of 9,659 incident breast cancer cases. The results of the fully adjusted linear analyses showed a HR (95% confidence interval) of 1.03 (1.00-1.06) per 10 μg/m³ NO2, 1.06 (1.01-1.11) per 5 μg/m³ PM2.5, 1.03 (0.99-1.06) per 0.5 10-5 m-1 BC, and 0.98 (0.94-1.01) per 10 μg/m³ O3. The effect estimates were most pronounced in the group of middle-aged women (50-54 years) and among never smokers.

CONCLUSIONS

The results were in support of an association between especially PM2.5 and breast cancer.

IMPACT

The findings of this study suggest a role of exposure to NO2, PM2.5, and BC in development of breast cancer.

Long-term air pollution exposure and Parkinson's disease mortality in a large pooled European cohort: An ELAPSE study

BACKGROUND

The link between exposure to ambient air pollution and mortality from cardiorespiratory diseases is well established, while evidence on neurodegenerative disorders including Parkinson's Disease (PD) remains limited.

OBJECTIVE

We examined the association between long-term exposure to ambient air pollution and PD mortality in seven European cohorts.

METHODS

Within the project 'Effects of Low-Level Air Pollution: A Study in Europe' (ELAPSE), we pooled data from seven cohorts among six European countries. Annual mean residential concentrations of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), black carbon (BC), and ozone (O₃), as well as 8 PM_2.5 components (copper, iron, potassium, nickel, sulphur, silicon, vanadium, zinc), for 2010 were estimated using Europe-wide hybrid land use regression models. PD mortality was defined as underlying cause of death being either PD, secondary Parkinsonism, or dementia in PD. We applied Cox proportional hazard models to investigate the associations between air pollution and PD mortality, adjusting for potential confounders.

RESULTS

Of 271,720 cohort participants, 381 died from PD during 19.7 years of follow-up. In single-pollutant analyses, we observed positive associations between PD mortality and PM_2.5 (hazard ratio per 5 µg/m³: 1.25; 95% confidence interval: 1.01-1.55), NO₂ (1.13; 0.95-1.34 per 10 µg/m³), and BC (1.12; 0.94-1.34 per 0.5 × 10^-5m^-1), and a negative association with O₃ (0.74; 0.58-0.94 per 10 µg/m³). Associations of PM_2.5, NO₂, and BC with PD mortality were linear without apparent lower thresholds. In two-pollutant models, associations with PM_2.5 remained robust when adjusted for NO₂ (1.24; 0.95-1.62) or BC (1.28; 0.96-1.71), whereas associations with NO₂ or BC attenuated to null. O₃ associations remained negative, but no longer statistically significant in models with PM_2.5. We detected suggestive positive associations with the potassium component of PM_2.5.

CONCLUSION

Long-term exposure to PM_2.5, at levels well below current EU air pollution limit values, may contribute to PD mortality.

Long-term exposure to traffic-related air pollution and stroke: A systematic review and meta-analysis

BACKGROUND

Stroke remains the second cause of death worldwide. The mechanisms underlying the adverse association of exposure to traffic-related air pollution (TRAP) with overall cardiovascular disease may also apply to stroke. Our objective was to systematically evaluate the epidemiological evidence regarding the associations of long-term exposure to TRAP with stroke.

METHODS

PubMed and LUDOK electronic databases were searched systematically for observational epidemiological studies from 1980 through 2019 on long-term exposure to TRAP and stroke with an update in January 2022. TRAP was defined according to a comprehensive protocol based on pollutant and exposure assessment methods or proximity metrics. Study selection, data extraction, risk of bias (RoB) and confidence assessments were conducted according to standardized protocols. We performed meta-analyses using random effects models; sensitivity analyses were assessed by geographic area, RoB, fatality, traffic specificity and new studies.

RESULTS

Nineteen studies were included. The meta-analytic relative risks (and 95% confidence intervals) were: 1.03 (0.98-1.09) per 1 μg/m³ EC, 1.09 (0.96-1.23) per 10 μg/m³ PM₁₀, 1.08 (0.89-1.32) per 5 μg/m³ PM_2.5, 0.98 (0.92; 1.05) per 10 μg/m³ NO₂ and 0.99 (0.94; 1.04) per 20 μg/m³ NO_x with little to moderate heterogeneity based on 6, 5, 4, 7 and 8 studies, respectively. The confidence assessments regarding the quality of the body of evidence and separately regarding the presence of an association of TRAP with stroke considering all available evidence were rated low and moderate, respectively.

CONCLUSION

The available literature provides low to moderate evidence for an association of TRAP with stroke.

Long term exposure to air pollution and kidney parenchyma cancer - Effects of low-level air pollution: a Study in Europe (ELAPSE)

BACKGROUND

Particulate matter (PM) is classified as a group 1 human carcinogen. Previous experimental studies suggest that particles in diesel exhaust induce oxidative stress, inflammation and DNA damage in kidney cells, but the evidence from population studies linking air pollution to kidney cancer is limited.

METHODS

We pooled six European cohorts (N = 302,493) to assess the association of residential exposure to fine particles (PM2.5), nitrogen dioxide (NO2), black carbon (BC), warm season ozone (O3) and eight elemental components of PM2.5 (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) with cancer of the kidney parenchyma. The main exposure model was developed for year 2010. We defined kidney parenchyma cancer according to the International Classification of Diseases 9th and 10th Revision codes 189.0 and C64. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level.

RESULTS

The participants were followed from baseline (1985-2005) to 2011-2015. A total of 847 cases occurred during 5,497,514 person-years of follow-up (average 18.2 years). Median (5-95%) exposure levels of NO2, PM2.5, BC and O3 were 24.1 μg/m3 (12.8-39.2), 15.3 μg/m3 (8.6-19.2), 1.6 10-5 m-1 (0.7-2.1), and 87.0 μg/m3 (70.3-97.4), respectively. The results of the fully adjusted linear analyses showed a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 0.92, 1.15) per 10 μg/m³ NO2, 1.04 (95% CI: 0.88, 1.21) per 5 μg/m³ PM2.5, 0.99 (95% CI: 0.89, 1.11) per 0.5 10-5 m-1 BCE, and 0.88 (95% CI: 0.76, 1.02) per 10 μg/m³ O3. We did not find associations between any of the elemental components of PM2.5 and cancer of the kidney parenchyma.

CONCLUSION

We did not observe an association between long-term ambient air pollution exposure and incidence of kidney parenchyma cancer.

Hyperlocal variation of nitrogen dioxide, black carbon, and ultrafine particles measured with Google Street View cars in Amsterdam and Copenhagen

Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO₂), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 - March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables. We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO₂. This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).

Long-term exposure to air pollution and mortality from dementia, psychiatric disorders, and suicide in a large pooled European cohort: ELAPSE study

Ambient air pollution is an established risk factor for premature mortality from chronic cardiovascular, respiratory and metabolic diseases, while evidence on neurodegenerative diseases and psychiatric disorders remains limited. We examined the association between long-term exposure to air pollution and mortality from dementia, psychiatric disorders, and suicide in seven European cohorts. Within the multicenter project 'Effects of Low-Level Air Pollution: A Study in Europe' (ELAPSE), we pooled data from seven European cohorts from six countries. Based on the residential addresses, annual mean levels of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), black carbon (BC), ozone (O₃), and 8 PM_2.5 components were estimated using Europe-wide hybrid land-use regression models. We applied stratified Cox proportional hazard models to investigate the associations between air pollution and mortality from dementia, psychiatric disorders, and suicide. Of 271,720 participants, 900 died from dementia, 241 from psychiatric disorders, and 164 from suicide, during a mean follow-up of 19.7 years. In fully adjusted models, we observed positive associations of NO₂ (hazard ratio [HR] = 1.38; 95 % confidence interval [CI]: 1.13, 1.70 per 10 µg/m³), PM_2.5 (HR = 1.29; 95 % CI: 0.98, 1.71 per 5 µg/m³), and BC (HR = 1.37; 95 % CI: 1.11, 1.69 per 0.5 × 10^-5/m) with psychiatric disorders mortality, as well as with suicide (NO₂: HR = 1.13 [95 % CI: 0.92, 1.38]; PM_2.5: HR = 1.19 [95 % CI: 0.76, 1.87]; BC: HR = 1.08 [95 % CI: 0.87, 1.35]), and no association with dementia mortality. We did not detect any positive associations of O₃ and 8 PM_2.5 components with any of the three mortality outcomes. Long-term exposure to NO₂, PM_2.5, and BC may lead to premature mortality from psychiatric disorders and suicide.

Associations between the urban exposome and type 2 diabetes: Results from penalised regression by least absolute shrinkage and selection operator and random forest models

BACKGROUND

Type 2 diabetes (T2D) is thought to be influenced by environmental stressors such as air pollution and noise. Although environmental factors are interrelated, studies considering the exposome are lacking. We simultaneously assessed a variety of exposures in their association with prevalent T2D by applying penalised regression Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Artificial Neural Networks (ANN) approaches. We contrasted the findings with single-exposure models including consistently associated risk factors reported by previous studies.

METHODS

Baseline data (n = 14,829) of the Occupational and Environmental Health Cohort study (AMIGO) were enriched with 85 exposome factors (air pollution, noise, built environment, neighbourhood socio-economic factors etc.) using the home addresses of participants. Questionnaires were used to identify participants with T2D (n = 676(4.6 %)). Models in all applied statistical approaches were adjusted for individual-level socio-demographic variables.

RESULTS

Lower average home values, higher share of non-Western immigrants and higher surface temperatures were related to higher risk of T2D in the multivariable models (LASSO, RF). Selected variables differed between the two multi-variable approaches, especially for weaker predictors. Some established risk factors (air pollutants) appeared in univariate analysis but were not among the most important factors in multivariable analysis. Other established factors (green space) did not appear in univariate, but appeared in multivariable analysis (RF). Average estimates of the prediction error (logLoss) from nested cross-validation showed that the LASSO outperformed both RF and ANN approaches.

CONCLUSIONS

Neighbourhood socio-economic and socio-demographic characteristics and surface temperature were consistently associated with the risk of T2D. For other physical-chemical factors associations differed per analytical approach.

Air pollution, white matter microstructure, and brain volumes: Periods of susceptibility from pregnancy to preadolescence

Air pollution exposure during early-life is associated with altered brain development, but the precise periods of susceptibility are unknown. We aimed to investigate whether there are periods of susceptibility of air pollution between conception and preadolescence in relation to white matter microstructure and brain volumes at 9-12 years old. We used data of 3515 children from the Generation R Study, a population-based birth cohort from Rotterdam, the Netherlands (2002-2006). We estimated daily levels of nitrogen dioxide (NO2), and particulate matter (PM2.5 and PM2.5absorbance) at participants' homes during pregnancy and childhood using land-use regression models. Diffusion tensor and structural brain images were obtained when children were 9-12 years of age, and we calculated fractional anisotropy and mean diffusivity, and several brain structure volumes. We performed distributed lag non-linear modeling adjusting for socioeconomic and lifestyle characteristics. We observed specific periods of susceptibility to all air pollutants from conception to age 5 years in association with lower fractional anisotropy and higher mean diffusivity that survived correction for multiple testing (e.g., -0.85 fractional anisotropy (95%CI -1.43; -0.27) per 5 μg/m³ increase in PM2.5 between conception and 4 years of age). We also observed certain periods of susceptibility to some air pollutants in relation to global brain and some subcortical brain volumes, but only the association between PM2.5 and putamen survived correction for multiple testing (172 mm³ (95%CI 57; 286) per 5 μg/m³ increase in PM2.5 between 4 months and 1.8 year of age). This study suggested that conception, pregnancy, infancy, toddlerhood, and early childhood seem to be susceptible periods to air pollution exposure for the development of white matter microstructure and the putamen volume. Longitudinal studies with repeated brain outcome measurements are needed for understanding the trajectories and the long-term effects of exposure to air pollution.

Ambient ultrafine particles and asthma onset until age 20: The PIAMA birth cohort

RATIONALE

Evidence regarding the role of long-term exposure to ultrafine particles (<0.1 μm, UFP) in asthma onset is scarce.

OBJECTIVES

We examined the association between exposure to UFP and asthma development in the Dutch PIAMA (Prevention and Incidence of Asthma and Mite Allergy) birth cohort and assessed whether there is an association with UFP, independent of other air pollutants.

METHODS

Data from birth up to age 20 years from 3687 participants were included. Annual average exposure to UFP at the residential addresses was estimated with a land-use regression model. Overall and age-specific associations of exposure at the birth address and current address at the time of follow-up with asthma incidence were assessed using discrete-time hazard models adjusting for potential confounders. We investigated both single- and two-pollutant models accounting for co-exposure to other air pollutants (PM_2.5 and PM₁₀ mass concentrations, nitrogen dioxide, and PM_2.5 absorbance).

MEASUREMENTS AND MAIN RESULTS

A total of 812 incident asthma cases were identified. Overall, we found that higher UFP exposure was associated with higher asthma incidence (adjusted odds ratio (95% confidence interval) 1.08 (1.02,1.14) and 1.06 (1.00, 1.12) per interquartile range increase in exposure at the birth address and current address at the time of follow-up, respectively). Age-specific associations were not consistent. The association was no longer significant after adjustment for other traffic-related pollutants (nitrogen dioxide and PM_2.5 absorbance).

CONCLUSIONS

Our findings support the importance of traffic-related air pollutants for asthma development through childhood and adolescence, but provide little support for an independent effect of UFP.

Ethnic and socioeconomic inequalities in relation to air pollution exposure in the Netherlands

Background

Air pollution (AP) contributes to a large disease burden and some populations are disproportionately exposed. It is unclear to what extent AP exposure differs across ethnic groups in the Netherlands and how this intersects with socioeconomic position (SEP). First, we identified differences in AP exposures between ethnic groups in the Netherlands. Second, we examined the interrelationships between ethnicity and SEP in relation to AP exposures.

Methods

We assessed AP exposures for residents of the Netherlands in 2019 (N = 17,251,511). Home address AP levels were estimated by dispersion models of the National Institute of Public Health and the Environment (RIVM). We linked exposure estimations of particulate matter &lt;10 or &lt; 2.5 μm (PM10, PM2.5), nitrogen dioxide (NO2), and elemental carbon (EC) to demographic data gathered by Statistics Netherlands. Absolute and relative differences in AP levels across ethnic groups were assessed. We conducted multivariable linear regression analyses and estimated marginal mean exposures to evaluate differences by ethnicity, SEP, age and sex within urban and rural areas. We tested for interactions and stratified accordingly.

Results

For the 40 largest minority ethnic groups (N &gt; 18,314 per group), exposure to all pollutants was higher than for ethnic Dutch, with up to 1.5-fold differences for NO2. After stratification for urbanity and SEP, ethnic exposure inequalities persisted. For ethnic Dutch and some migrant groups, we found the lowest AP exposures in the middle SEP group (i.e. U-shaped trends), while we found linear patterns in other large migrant groups, with higher exposures at lower SEP.

Conclusions

Exposure to PM10, PM2.5, NO2, and EC was consistently higher in minority ethnic groups compared to ethnic Dutch. The association between SEP and AP levels showed different patterns between the majority ethnic Dutch and some of the largest minority ethnic groups. Further research is needed to define the equity and health implications.

Key messages

• Minority ethnic groups in the Netherlands are consistently exposed to higher levels of air pollution (PM10, PM2.5, NO2, and EC) than the ethnic Dutch population.

• Depending on the ethnic group, the association between SEP and air pollution exposure was either linear (i.e. lower exposures at higher SEP) or U-shaped (i.e. lower exposures in the middle SEP group).

A Knowledge Transfer Approach to Map Long-Term Concentrations of Hyperlocal Air Pollution from Short-Term Mobile Measurements

Mobile measurements are increasingly used to develop spatially explicit (hyperlocal) air quality maps using land-use regression (LUR) models. The prevailing design of mobile monitoring campaigns results in the collection of short-term, on-road air pollution measurements during daytime on weekdays. We hypothesize that LUR models trained with such mobile measurements are not optimized for estimating long-term average residential air pollution concentrations. To bridge the knowledge gaps in space (on-road versus near-road) and time (short- versus long-term), we propose transfer-learning techniques to adapt LUR models by transferring the mobile knowledge into long-term near-road knowledge in an end-to-end manner. We trained two transfer-learning LUR models by incorporating mobile measurements of nitrogen dioxide (NO₂) and ultrafine particles (UFP) collected by Google Street View cars with long-term near-road measurements from regular monitoring networks in Amsterdam. We found that transfer-learning LUR models performed 55.2% better in predicting long-term near-road concentrations than the LUR model trained only with mobile measurements for NO₂ and 26.9% for UFP, evaluated by normalized mean absolute errors. This improvement in model accuracy suggests that transfer-learning models provide a solution for narrowing the knowledge gaps and can improve the accuracy of mapping long-term near-road air pollution concentrations using short-term on-road mobile monitoring data.

Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.