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.

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.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment.

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Hourly land-use regression modeling for NO2 and PM2.5 in the Netherlands.

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016-2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50-0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24-0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays'. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35-0.70; PM2.5 hourly R2 = 0.01-0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.

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.

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 ≤2.5 µm (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 5 µg/m3, BC (1.26 (1.03-1.53) per 0.5 × 10- 5/m), NO2 (1.13 (0.93-1.38) per 10 µg/m3) and inverse for O3 (0.71 (0.54-0.93) per 10 µg/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.

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.

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.

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.

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.

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, PM2.5, PM2.5 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, PM2.5, PM2.5 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, PM2.5, PM2.5 absorbance and PM10 were not associated with sensitization to inhalant allergens. For NO2, PM2.5 and PM2.5 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, PM2.5, PM2.5 absorbance and PM10 were associated with sensitization to food allergens.

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 (PM2.5) is a well-recognized risk factor for premature death. However, evidence on which PM2.5 components are most relevant is unclear. METHODS: We evaluated the associations between mortality and long-term exposure to eight PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 components for different causes of death, with K and Si seeming to be most consistently associated with mortality in Denmark.

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.

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 PM2.5, soot, NOx, 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 PM2.5 concentration was almost eight times higher than the current WHO guideline and the NO2 level was three times higher. Soot and NOx 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 NOx. Outdoor air pollutants were not significantly associated with reduced birth weight nor the risk of LBW. CONCLUSION: Exposure to soot and NOx 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.

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 PM2.5, PM2.5 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 PM2.5 components were associated with natural mortality, with hazard ratios (95% confidence intervals) of 1.026 (1.015, 1.038) per 5 µg/m3 PM2.5, 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 PM2.5 was only associated with CVD mortality. Natural mortality associations persisted < 20 µg/m3 for PM2.5 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.

Long-term Air Pollution Exposure and Pneumonia-related Mortality in a Large Pooled European Cohort.

Rationale: Ambient air pollution exposure has been linked to mortality from chronic cardiorespiratory diseases, while evidence on respiratory infections remains more limited. Objectives: We examined the association between long-term exposure to air pollution and pneumonia-related mortality in adults in a pool of eight European cohorts. Methods: Within the multicenter project ELAPSE (Effects of Low-Level Air Pollution: A Study in Europe), we pooled data from eight cohorts among six European countries. Annual mean residential concentrations in 2010 for fine particulate matter, nitrogen dioxide (NO2), black carbon (BC), and ozone 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 pneumonia, influenza, and acute lower respiratory infections (ALRI) mortality. Measurements and Main Results: Of 325,367 participants, 712 died from pneumonia and influenza combined, 682 from pneumonia, and 695 from ALRI during a mean follow-up of 19.5 years. NO2 and BC were associated with 10-12% increases in pneumonia and influenza combined mortality, but 95% confidence intervals included unity (hazard ratios, 1.12 [0.99-1.26] per 10 µg/m3 for NO2; 1.10 [0.97-1.24] per 0.5 10-5m-1 for BC). Associations with pneumonia and ALRI mortality were almost identical. We detected effect modification suggesting stronger associations with NO2 or BC in overweight, employed, or currently smoking participants compared with normal weight, unemployed, or nonsmoking participants. Conclusions: Long-term exposure to combustion-related air pollutants NO2 and BC may be associated with mortality from lower respiratory infections, but larger studies are needed to estimate these associations more precisely.

Long-term exposure to ambient air pollution and bladder cancer incidence in a pooled European cohort: the ELAPSE project.

BACKGROUND: The evidence linking ambient air pollution to bladder cancer is limited and mixed. METHODS: We assessed the associations of bladder cancer incidence with residential exposure to fine particles (PM2.5), nitrogen dioxide (NO2), black carbon (BC), warm season ozone (O3) and eight PM2.5 elemental components (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in a pooled cohort (N = 302,493). Exposures were primarily assessed based on 2010 measurements and back-extrapolated to the baseline years. We applied Cox proportional hazard models adjusting for individual- and area-level potential confounders. RESULTS: During an average of 18.2 years follow-up, 967 bladder cancer cases occurred. We observed a positive though statistically non-significant association between PM2.5 and bladder cancer incidence. Hazard Ratios (HR) were 1.09 (95% confidence interval (CI): 0.93-1.27) per 5 µg/m3 for 2010 exposure and 1.06 (95% CI: 0.99-1.14) for baseline exposure. Effect estimates for NO2, BC and O3 were close to unity. A positive association was observed with PM2.5 zinc (HR 1.08; 95% CI: 1.00-1.16 per 10 ng/m3). CONCLUSIONS: We found suggestive evidence of an association between long-term PM2.5 mass exposure and bladder cancer, strengthening the evidence from the few previous studies. The association with zinc in PM2.5 suggests the importance of industrial emissions.

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.

Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO2 Concentrations Using Measurements Sampled with Google Street View Cars.

High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e., land use regression (LUR) models]. Assuming that all road segments are measured, we developed a mixed model framework that predicts concentrations by an LUR model, while allowing road segments to deviate from the LUR prediction based on between-segment variation as a random effect. We used Google Street View cars, equipped with high-quality AQ instruments, and measured the concentration of NO2 on every street in Amsterdam (n = 46.664) and Copenhagen (n = 28.499) on average seven times over the course of 9 and 16 months, respectively. We compared the data-only mapping, LUR, and mixed model estimates with measurements from passive samplers (n = 82) and predictions from dispersion models in the same time window as mobile monitoring. In Amsterdam, mixed model estimates correlated rs (Spearman correlation) = 0.85 with external measurements, whereas the data-only approach and LUR model estimates correlated rs = 0.74 and 0.75, respectively. Mixed model estimates also correlated higher rs = 0.65 with the deterministic model predictions compared to the data-only (rs = 0.50) and LUR model (rs = 0.61). In Copenhagen, mixed model estimates correlated rs = 0.51 with external model predictions compared to rs = 0.45 and rs = 0.50 for data-only and LUR model, respectively. Correlation increased for 97 locations (rs = 0.65) with more detailed traffic information. This means that the mixed model approach is able to combine the strength of data-only mapping (to show hyperlocal variation) and LUR models by shrinking uncertain concentrations toward the model output.

Long-Term Exposure to Source-Specific Fine Particles and Mortality─A Pooled Analysis of 14 European Cohorts within the ELAPSE Project.

We assessed mortality risks associated with source-specific fine particles (PM2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM2.5 components was assessed at baseline residential addresses. The source apportionment resulted in the identification of five sources: traffic, residual oil combustion, soil, biomass and agriculture, and industry. In single-source analysis, all identified sources were significantly positively associated with increased natural mortality risks. In multisource analysis, associations with all sources attenuated but remained statistically significant with traffic, oil, and biomass and agriculture. The highest association per interquartile increase was observed for the traffic component (HR: 1.06; 95% CI: 1.04 and 1.08 per 2.86 µg/m3 increase) across five identified sources. On a 1 µg/m3 basis, the residual oil-related PM2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM2.5 mass, suggesting that past estimates using the generic PM2.5 exposure response function have underestimated the potential clean air health benefits of reducing fossil-fuel combustion. Source-specific associations with cause-specific mortality were in general consistent with findings of natural mortality.