Search

Implementation of Daylight Savings Time (DST) started in various jurisdictions during the first half of the 20th century. Much debate on the merits of the twice-yearly change in the official local time has ensued since then regarding energy usage, sleep patterns, health outcomes, traffic safety, etc. The DST switch in the official time abruptly shifts anthropogenic emissions that are related to human activities relative to the Coordinated Universal Time (UTC) but does not affect meteorological processes that disperse them, which day-to-day variability is affected by longer time scales. Here, we utilized the DST clock changes as a repeating biannual experiment to study the impact of meteorology on air pollution. We analyzed traffic volume data and up to 20 years of nitrogen oxides (NOx), fine particulate matter (PM2.5), ozone (O3) and carbon monoxide (CO) concentrations, observed at different air quality monitoring (AQM) stations in Israel, demonstrating a clear and significant impact of the daily meteorological cycle on traffic related air pollution (TRAP). In particular, traffic emissions are prerequisite for TRAP, but meteorology was found to dominate the daily patterns of the NOx, O3 and CO concentration fields in the study area. On the other hand, the impact of vehicle emissions on PM2.5 concentrations seems to be very small. Our results highlight the multiscale interplay between pollutant emissions and dispersion processes, especially for pollutants that are emitted near the surface. We demonstrate that while DST clock changes do not affect the emissions intensity, nor the meteorological processes vigor, they do shift human activity-related emissions with respect to the DST-blind dispersion processes. This results in short-term effects on primary traffic-related pollutant concentrations that cancel out over the day, and a consistent yet small effect on secondary traffic-related pollutant concentrations (O3). [ABSTRACT FROM AUTHOR]

This study examines uncertainties in the retrieval of the Aerosol Optical Depth (AOD) for different aerosol types, which are obtained from different satellite-borne aerosol retrieval products over North Africa, California, Germany, and India and Pakistan in the years 2007–2019. In particular, we compared the aerosol types reported as part of the AOD retrieval from MODIS/MAIAC and CALIOP, with the latter reporting richer aerosol types than the former, and from the Ozone Monitoring Instrument (OMI) and MODIS Deep Blue (DB), which retrieve aerosol products at a lower spatial resolution than MODIS/MAIAC. Whereas MODIS and OMI provide aerosol products nearly every day over of the study areas, CALIOP has only a limited surface footprint, which limits using its data products together with aerosol products from other platforms for, e.g., estimation of surface particulate matter (PM) concentrations. In general, CALIOP and MAIAC AOD showed good agreement with the AERONET AOD (r: 0.708, 0.883; RMSE: 0.317, 0.123, respectively), but both CALIOP and MAIAC AOD retrievals were overestimated (36–57%) with respect to the AERONET AOD. The aerosol type reported by CALIOP (an active sensor) and by MODIS/MAIAC (a passive sensor) were examined against aerosol types derived from a combination of satellite data products retrieved by MODIS/DB (Angstrom Exponent, AE) and OMI (Aerosols Index, AI, the aerosol absorption at the UV band). Together, the OMI-DB (AI-AE) classification, which has wide spatiotemporal cover, unlike aerosol types reported by CALIOP or derived from AERONET measurements, was examined as auxiliary data for a better interpretation of the MAIAC aerosol type classification. Our results suggest that the systematic differences we found between CALIOP and MODIS/MAIAC AOD were closely related to the reported aerosol types. Hence, accounting for the aerosol type may be useful when predicting surface PM and may allow for the improved quantification of the broader environmental impacts of aerosols, including on air pollution and haze, visibility, climate change and radiative forcing, and human health. [ABSTRACT FROM AUTHOR]

Childhood exposure to nitrogen oxides (NOx) is considered a risk factor for the onset of asthma. However, associations of this exposure with other atopic diseases and factors that modify this association are less clear. We aimed to study associations between exposure to NOx and the prevalence of atopic diseases in Israeli adolescents using a cross-sectional design. The study population comprised all Israeli-born adolescents whose medical status was evaluated for mandatory military recruitment during 1967-2017 (n = 2,523,745), of whom 5.9% had prevalent asthma. We based the exposure assessments on a land-use regression model and estimated associations using multivariable logistic regression models. Across all periods, mean exposure to NOx from birth to adolescence was associated with prevalent asthma at the examination in a dose-response manner, with an odds ratio for the upper quintile of 1.61 (95% CI: 1.56-1.67), in comparison to the lowest quintile. Associations were stronger in males and in lower socioeconomic strata. We found the strongest associations for asthma with comorbid rhinitis, with an almost twofold increase in the odds of upper versus lower quintile of exposure (odds ratio = 1.96, 95% CI: 1.82-2.11). Rhino-conjunctivitis and allergic atopic dermatitis suggested a possible threshold level with NOx. Capsule Summary: Research indicates that half of the global population will suffer from an allergic condition at some point in life. Childhood exposure to nitrogen oxides is a risk factor for the onset of asthma. The association between exposure and allergic diseases other than asthma is unclear. We demonstrate a strong, dose-response relationship between exposure and a group of allergic outcomes, using data comprising 2.5 million subjects over 50 years. The large health benefits from clean air should motivate governments to prioritize mitigation measures. [ABSTRACT FROM AUTHOR]

The evaluation of the effects of air pollution on public health and human-wellbeing requires reliable data. Standard air quality monitoring stations provide accurate measurements of airborne pollutant levels, but, due to their sparse distribution, they cannot capture accurately the spatial variability of air pollutant concentrations within cities. Dedicated in-depth field campaigns have dense spatial coverage of the measurements but are held for relatively short time periods. Hence, their representativeness is limited. Moreover, the oftentimes integrated measurements represent time-averaged records. Recent advances in communication and sensor technologies enable the deployment of dense grids of Wireless Distributed Environmental Sensor Networks for air quality monitoring, yet their capability to capture urban-scale spatiotemporal pollutant patterns has not been thoroughly examined to date. Here, we summarize our studies on the practicalities of using data streams from sensor nodes for air quality measurement and the required methods to tune the results to different stakeholders and applications. We summarize the results from eight cities across Europe, five sensor technologies-three stationary (with one tested also while moving) and two personal sensor platforms, and eight ambient pollutants. Overall, few sensors showed an exceptional and consistent performance, which can shed light on the fine spatiotemporal urban variability of pollutant concentrations. Stationary sensor nodes were more reliable than personal nodes. In general, the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. This calls for the development of suitable field calibration procedures, and several such in situ field calibrations are presented. [ABSTRACT FROM AUTHOR]

Haifa Bay Area (HBA) contains Israel's principal industrial area, and there are substantial public concerns about health effects from its emissions. We aimed to examine associations between exposure to air pollution from HBA industrial area with prevalent asthma and other atopic diseases at age 17. This is a cross-sectional study. The study population included all adolescents born in Israel and whose medical status was evaluated for mandatory military recruitment by the Israeli medical corps during 1967–2017. We analyzed prevalent asthma, allergic rhinitis, atopic dermatitis, and rhinoconjunctivitis. We estimated exposure to industrial air pollution by a kriging interpolation of historical SO2 observations and adjusted the associations to the year of birth, SES, school orientation, and traffic pollution. The study population included n = 2,523,745 adolescents, among which 5.9% had prevalent asthma and 4.6% had allergic rhinitis. Residency in HBA was associated with a higher adjusted risk of asthma, compared with non-HBA residency. Still, this association was limited to the three lowest exposure categories, while the highest exposure group had the lowest adjusted risk. Sensitivity analyses and other atopic diseases presented similar results. These results do not provide support for causal relationships between HBA industry-related emissions and prevalent atopic diseases. [ABSTRACT FROM AUTHOR]

Exposure to airborne particles is a risk factor of many short- and long-term health effects. Most epidemiological studies include estimates of exposure to ambient particles, however, people living in developed countries spend most of their time indoors. This work presents an analysis of a field campaign of simultaneous measurements of indoor-to-outdoor particle number concentrations (PNCs) in Israel. Fine and coarse PNCs were continuously measured using Dylos DC1700 devices from October 2016 to October 2017. The median outdoor PNC was always higher than the indoor PNC in all the five sampling locations. Outdoor fine PNCs peak during the night and experience a trough in the afternoon. The median of the fine indoor-to-outdoor PNC ratio (IOR) was 0.83, with an inter quartile range (IQR) of 0.59. The median of the coarse IOR was 0.70, with an IQR of 0.77. Lower IORs were experienced at night than during the day, with a daily peak (IOR > 1) around noon. Information about the IOR in different regions and seasons may help epidemiologists and policy makers understand the true health effects of particulate air pollution, and correct their exposure estimations such that they account for indoor exposure as well. [ABSTRACT FROM AUTHOR]