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

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

Ultrafine particles, particle components and lung function at age 16 years:The PIAMA birth cohort study

Background: Particulate matter (PM) air pollution exposure has been linked to lung function in adolescents, but little is known about the relevance of specific PM components and ultrafine particles (UFP). Objectives: To investigate the associations of long-term exposure to PM elemental composition and UFP with lung function at age 16 years. Methods: For 706 participants of a prospective Dutch birth cohort, we assessed associations of forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) at age 16 with average exposure to eight elemental components (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc) in PM2.5 and PM10, as well as UFP during the preceding years (age 13–16 years) estimated by land-use regression models. After assessing associations for each pollutant individually using linear regression models with adjustment for potential confounders, independence of associations with different pollutants was assessed in two-pollutant models with PM mass and NO2, for which associations with lung function have been reported previously. Results: We observed that for most PM elemental components higher exposure was associated with lower FEV1, especially PM10 sulfur [e.g. adjusted difference −2.23% (95% confidence interval (CI) −3.70 to −0.74%) per interquartile range (IQR) increase in PM10 sulfur]. The association with PM10 sulfur remained after adjusting for PM10 mass. Negative associations of exposure to UFP with both FEV1 and FVC were observed [-1.06% (95% CI: −2.08 to −0.03%) and −0.65% (95% CI: −1.53 to 0.23%), respectively per IQR increase in UFP], but did not persist in two-pollutant models with NO2 or PM2.5. Conclusions: Long-term exposure to sulfur in PM10 may result in lower FEV1 at age 16. There is no evidence for an independent effect of UFP exposure

Совместная обработка траекторно измерительной информации при испытаниях сложных информационно-управляющих систем

Рассмотрен метод траекторных измерений, использующий совместную обработку измерительной информации, полученной от полигонных средств внешнетраекторных измерений и специальной бортовой измерительной аппаратуры при натурных испытаниях сложных информационно-управляющих систем на местах их постоянной дислокации.A method of trajectory measurements, which uses a joint processing of the measuring data, obtained from the proving ground means of external trajectory measurements and special onboard measuring equipment with the full-scale tests of the complex information-control systems at their constant disposition is considered

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 (NO 2) concentrations in Amsterdam. The airshed countries scale provided the lowest absolute errors, and the Europe-wide scale had the highest R 2. 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/m 3) and improved the percentage explained variances compared to the global model (R 2, 0.43 vs 0.28, assessed by independent long-term NO 2 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

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 (PM2.5 and PM10 mass concentrations, nitrogen dioxide, and PM2.5 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 PM2.5 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

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 NO 2, PM 2.5, PM 2.5 absorbance and PM 10 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). NO 2, PM 2.5, PM 2.5 absorbance and PM 10 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). NO 2, PM 2.5, PM 2.5 absorbance and PM 10 were not associated with sensitization to inhalant allergens. For NO 2, 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. NO 2, PM 2.5, PM 2.5 absorbance and PM 10 were associated with sensitization to food allergens

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 (NO 2), 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 NO 2. 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 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 (NO 2) and fine particles (PM 2 . 5 and PM 10), 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 (NO 2)). 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/cm 3) 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

Environmental risk factors of type 2 diabetes-an exposome approach

Type 2 diabetes is one of the major chronic diseases accounting for a substantial proportion of disease burden in Western countries. The majority of the burden of type 2 diabetes is attributed to environmental risks and modifiable risk factors such as lifestyle. The environment we live in, and changes to it, can thus contribute substantially to the prevention of type 2 diabetes at a population level. The ‘exposome’ represents the (measurable) totality of environmental, i.e. nongenetic, drivers of health and disease. The external exposome comprises aspects of the built environment, the social environment, the physico-chemical environment and the lifestyle/food environment. The internal exposome comprises measurements at the epigenetic, transcript, proteome, microbiome or metabolome level to study either the exposures directly, the imprints these exposures leave in the biological system, the potential of the body to combat environmental insults and/or the biology itself. In this review, we describe the evidence for environmental risk factors of type 2 diabetes, focusing on both the general external exposome and imprints of this on the internal exposome. Studies provided established associations of air pollution, residential noise and area-level socioeconomic deprivation with an increased risk of type 2 diabetes, while neighbourhood walkability and green space are consistently associated with a reduced risk of type 2 diabetes. There is little or inconsistent evidence on the contribution of the food environment, other aspects of the social environment and outdoor temperature. These environmental factors are thought to affect type 2 diabetes risk mainly through mechanisms incorporating lifestyle factors such as physical activity or diet, the microbiome, inflammation or chronic stress. To further assess causality of these associations, future studies should focus on investigating the longitudinal effects of our environment (and changes to it) in relation to type 2 diabetes risk and whether these associations are explained by these proposed mechanisms. Graphical abstract: [Figure not available: see fulltext.