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

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

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

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

Acute respiratory health effects of air pollution on asthmatic adolescents residing in a community in close proximity to-mine dump in South Africa : panel study

Air pollution arising from mine dumps has been a major public health concern to communities located in close proximity to these facilities in South Africa. The study investigated the association between acute changes in lung function and ambient air pollutants on asthmatic children in Noordgesig, Gauteng, South Africa. A panel study design with repeated measures was used to carry out the investigation which involved 15 asthmatic children. Each participating child completed an asthma daily symptom diary and performed forced expiratory flows in one second (FEV1) for 21 consecutive days. The 24 h ambient air pollution concentrations were monitored over this period. Linear mixed effect models adjusted for temperature, relative humidity, the day of the week, first order autocorrelation and 10 μg.m-3 increase of the mean of pollutant concentrations were used to determine the association between morning FEV1 and air pollutants. The association between air pollutants, respiratory symptoms and medication use were evaluated with logistic mixed effect models. The mean 24-hour concentration of NOx for current day was 0.762% (95% CI: -1.296 – -0.227), and for O3, the respective current and previous days were 0.780% (95% CI: -1.461 – -0.099) and 0.716% (95% CI: -1.386 – -0.045), all of these were significantly associated with the morning FEV1 decline. Single pollutant models showed significant positive associations between chest tightness, cough and NO2, O3, NOx, and SO2 pollutants. Medication use such as corticosteroids and short-acting β2 agonist were associated with NOx (OR = 1.07; 95% CI: 1.00 – 1.28) and O3 (OR = 1.57 95% CI: 1.03 – 2.72) respectively. Interestingly, a protective significant effect, was observed between SO2 and cough (OR = 0.45; 95% CI: 0.21 – 0.97). The findings of this study provide evidence that an acute change of gaseous air pollutants in communities situated near mine dumps exacerbates lung function in vulnerable children.Funding for the field survey came from the Mine Health Safety Council of South Africa (MHSCSA) and National Research Fund – Deutscher Akademischer Austausch Dienst (NRF – DAAD).http://www.journalissues.org/IRJPEH/am2017School of Health Systems and Public Health (SHSPH

Influenza in long-term Dutch travelers in the tropics: Symptoms and infections

Background: Influenza is a common infection among travelers, and attack rates are well documented in short-term travelers and holiday makers. Little data exists on long-term, non-expatriate travelers. Methods: This was a prospective mono-centre study of immunocompetent, Dutch travelers aged ≥18 to 64 years. It was conducted at the Public Health Service travel clinic in Amsterdam from December 2008 to September 2011, and included all travelers intending to travel to a tropical or sub-tropical country. Results: Among 602 Dutch long-term travelers to tropical regions, 82 % had protective influenza antibody titres pre-travel. The influenza attack rate of serologically confirmed infection during travel was 15 %, and of symptomatic infection was 6.3 % (fever alone) and 2 % (ILI), respectively. Conclusions: The attack rate in this study is similar to seasonal rates of infection in the general population. Influenza vaccination pre-travel is therefore most important for people at risk of medical complications due to influenza

Commuters’ Exposure to Particulate Matter Air Pollution Is Affected by Mode of Transport, Fuel Type, and Route

Background: Commuters are exposed to high concentrations of air pollutants, but little quantitative information is currently available on differences in exposure between different modes of transport, routes, and fuel types.Objectives: The aim of our study was to assess differences in commuters' exposure to traffic-related air pollution related to transport mode, route, and fuel type.Methods: We measured particle number counts (PNCs) and concentrations of PM2.5 (particulate matte

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